references.bib

%% MICRO50

@inproceedings{li2017drisa,
  title={DRISA: A DRAM-based Reconfigurable In-Situ Accelerator},
  author={Li, Shuangchen and Niu, Dimin and Malladi, Krishna T and Zheng, Hongzhong and Brennan, Bob and Xie, Yuan},
  booktitle={Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture},
  pages={288--301},
  year={2017},
  organization={ACM}
}

@inproceedings{park2017scaleout,
  title={Scale-out acceleration for machine learning},
  author={Park, Jongse and Sharma, Hardik and Mahajan, Divya and Kim, Joon Kyung and Olds, Preston and Esmaeilzadeh, Hadi},
  booktitle={Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture},
  pages={367--381},
  year={2017},
  organization={ACM}
}

@inproceedings{hill2017deftnn,
  title={DeftNN: addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and near-compute data fission},
  author={Hill, Parker and Jain, Animesh and Hill, Mason and Zamirai, Babak and Hsu, Chang-Hong and Laurenzano, Michael A and Mahlke, Scott and Tang, Lingjia and Mars, Jason},
  booktitle={Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture},
  pages={786--799},
  year={2017},
  organization={ACM}
}

%% MICRO49

@inproceedings{caulfield2016cloudscale,
  title={A cloud-scale acceleration architecture},
  author={Caulfield, Adrian M and Chung, Eric S and Putnam, Andrew and Angepat, Hari and Fowers, Jeremy and Haselman, Michael and Heil, Stephen and Humphrey, Matt and Kaur, Puneet and Kim, Joo-Young and others},
  booktitle={Microarchitecture (MICRO), 2016 49th Annual IEEE/ACM International Symposium on},
  pages={1--13},
  year={2016},
  organization={IEEE}
}

%% ASPLOS 2017

@inproceedings{gao2017tetris,
  title={TETRIS: Scalable and Efficient Neural Network Acceleration with 3D Memory},
  author={Gao, Mingyu and Pu, Jing and Yang, Xuan and Horowitz, Mark and Kozyrakis, Christos},
  booktitle={Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems},
  pages={751--764},
  year={2017},
  organization={ACM}
}

@inproceedings{rajbhandari2017optimizing,
  title={Optimizing CNNs on Multicores for Scalability, Performance and Goodput},
  author={Rajbhandari, Samyam and He, Yuxiong and Ruwase, Olatunji and Carbin, Michael and Chilimbi, Trishul},
  booktitle={Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems},
  pages={267--280},
  year={2017},
  organization={ACM}
}

%% ISCA 2016

@inproceedings{albericio2016cnvlutin, 
author={J. Albericio and P. Judd and T. Hetherington and T. Aamodt and N. E. Jerger and A. Moshovos}, 
booktitle={2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)}, 
title={Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing}, 
year={2016}, 
volume={}, 
number={}, 
pages={1-13}, 
keywords={decision making;memory architecture;neural nets;parallel architectures;performance evaluation;power aware computing;Cnvlutin;DNN;ED2P;EDP;computation elimination decisions;data parallel units;data storage format;data storage format encoding;data-parallel architecture;energy delay product;energy delay squared product;energy efficiency;hardware acceleration;hierarchical data-parallel units;image classification;ineffectual-neuron-free deep neural network computing;memory hierarchy;value-based approach;zero-valued operand multiplications;Acceleration;Computer architecture;Delays;Feature extraction;Hardware;Neural networks;Neurons}, 
doi={10.1109/ISCA.2016.11}, 
ISSN={1063-6897}, 
month={June},}

@inproceedings{shafiee2016isaac, 
author={A. Shafiee and A. Nag and N. Muralimanohar and R. Balasubramonian and J. P. Strachan and M. Hu and R. S. Williams and V. Srikumar}, 
booktitle={2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)}, 
title={ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars}, 
year={2016}, 
volume={}, 
number={}, 
pages={14-26}, 
keywords={DRAM chips;digital arithmetic;learning (artificial intelligence);memristor circuits;neural nets;ADC;DaDianNao architecture;ISAAC architecture;analog-to-digital conversion;convolutional neural network accelerator;data encoding;digital arithmetic operations;dot-product operations;eDRAM banks;in-situ analog arithmetic crossbars;in-situ processing approach;machine learning algorithms;memristor crossbar arrays;memristor storage;pipelined architecture design;Biological neural networks;Computer architecture;Kernel;Machine learning algorithms;Memristors;Neurons;Pipelines;CNN;DNN;accelerator;analog;memristor;neural}, 
doi={10.1109/ISCA.2016.12}, 
ISSN={1063-6897}, 
month={June},}

@inproceedings{han2016eie, 
author={S. Han and X. Liu and H. Mao and J. Pu and A. Pedram and M. A. Horowitz and W. J. Dally}, 
booktitle={2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)}, 
title={EIE: Efficient Inference Engine on Compressed Deep Neural Network}, 
year={2016}, 
volume={}, 
number={}, 
pages={243-254}, 
keywords={DRAM chips;SRAM chips;matrix multiplication;neural nets;sparse matrices;AlexNet;DNN;DRAM;EIE;VGGNet;compressed deep neural network;embedded system;energy efficient inference engine;onchip SRAM;power dissipation;sparse matrix-vector multiplication;weight sharing;Acceleration;Computational modeling;Hardware;Neural networks;Random access memory;Sparse matrices;System-on-chip;ASIC;Algorithm-Hardware co-Design;Deep Learning;Hardware Acceleration;Model Compression}, 
doi={10.1109/ISCA.2016.30}, 
ISSN={1063-6897}, 
month={June},}

@inproceedings{likamwa2016redeye, 
author={R. LiKamWa and Y. Hou and Y. Gao and M. Polansky and L. Zhong}, 
booktitle={2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)}, 
title={RedEye: Analog ConvNet Image Sensor Architecture for Continuous Mobile Vision}, 
year={2016}, 
volume={}, 
number={}, 
pages={255-266}, 
keywords={computer vision;image sensors;neural nets;quantisation (signal);ConvNet image sensor architecture;RedEye;algorithmic cyclic reuse;analog domain;analog readout circuitry;cloudlet-based system energy reduction;column-parallel design;computation-based system energy reduction;continuous mobile vision;convolutional neural network;data traffic;image frame capturing;intensive computation;physical design reuse;programmable mechanisms;quantization processing;sensor energy reduction;vision feature processing;vision processing;Analog circuits;Arrays;Complexity theory;Convolution;Image sensors;Mobile communication;Neurons;computer vision;continuous mobile vision;pre-quantization processing;programmable analog computing}, 
doi={10.1109/ISCA.2016.31}, 
ISSN={1063-6897}, 
month={June},}

@inproceedings{reagen2016minerva, 
author={B. Reagen and P. Whatmough and R. Adolf and S. Rama and H. Lee and S. K. Lee and J. M. Hernández-Lobato and G. Y. Wei and D. Brooks}, 
booktitle={2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)}, 
title={Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators}, 
year={2016}, 
volume={}, 
number={}, 
pages={267-278}, 
keywords={neural nets;DNN hardware accelerators;DNN model accuracy;Minerva;SRAM voltages;active hardware fault detection;automated codesign;classification tasks;deep neural network accelerators;deep neural networks;domain-aware error mitigation;fixed-point accelerator baseline;general-purpose hardware;heterogeneous datatype optimization;inline predication;magnitude improvement;mobile devices;power-constrained IoT;small activity values;specialized hardware;ultra-low power DNN accelerators;Circuit faults;Hardware;Integrated circuit modeling;Libraries;Optimization;Random access memory;Space exploration}, 
doi={10.1109/ISCA.2016.32}, 
ISSN={1063-6897}, 
month={June},}

@inproceedings{chen2016eyeriss, 
author={Y. H. Chen and J. Emer and V. Sze}, 
booktitle={2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)}, 
title={Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks}, 
year={2016}, 
volume={}, 
number={}, 
pages={367-379}, 
keywords={computer architecture;convolution;data flow computing;neural nets;power aware computing;AlexNet CNN configurations;Eyeriss;PE local storage;RS dataflow;computational complexity;data movement energy consumption minimization;deep CNNs;deep convolutional neural networks;direct interPE communication;energy-efficient CNN processing;energy-efficient dataflow;feature map pixels;high-dimensional convolutions;local data reuse;parallel processing;partial sum accumulations;processing engine local storage;row-stationary dataflow;spatial architecture;spatial parallelism;Arrays;Parallel processing;Radio frequency;Random access memory;Shape;Throughput;Convolutional Neural Networks;Dataflow;Energy Efficiency;Spatial Architecture}, 
doi={10.1109/ISCA.2016.40}, 
ISSN={1063-6897}, 
month={June},}

@inproceedings{kim2016neurocube, 
author={D. Kim and J. Kung and S. Chai and S. Yalamanchili and S. Mukhopadhyay}, 
booktitle={2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)}, 
title={Neurocube: A Programmable Digital Neuromorphic Architecture with High-Density 3D Memory}, 
year={2016}, 
volume={}, 
number={}, 
pages={380-392}, 
keywords={logic circuits;memory architecture;neural nets;storage management chips;HMC;Neurocube;convolutional neural network;high-density 3D memory;logic tier;memory centric computing;neural computing;programmable digital neuromorphic architecture;Artificial neural networks;Biological neural networks;Computer architecture;Neurons;Random access memory;Three-dimensional displays;Neural nets;Neurocomputers;Neuromorphic computing}, 
doi={10.1109/ISCA.2016.41}, 
ISSN={1063-6897}, 
month={June},}

@inproceedings{liu2016cambricon, 
author={S. Liu and Z. Du and J. Tao and D. Han and T. Luo and Y. Xie and Y. Chen and T. Chen}, 
booktitle={2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)}, 
title={Cambricon: An Instruction Set Architecture for Neural Networks}, 
year={2016}, 
volume={}, 
number={}, 
pages={393-405}, 
keywords={instruction sets;neural net architecture;power aware computing;Cambricon;NN high-level functional blocks;TSMC 65nm technology;application-specific hardware accelerators;control instructions;data transfer instructions;domain-specific ISA;domain-specific instruction set architecture;energy-efficiency;general-purpose processors;load-store architecture;logical instructions;matrix instructions;neural networks;scalar instructions;vector instructions;Artificial neural networks;Computer architecture;Data transfer;Ground penetrating radar;Libraries;Registers;System-on-chip}, 
doi={10.1109/ISCA.2016.42}, 
ISSN={1063-6897}, 
month={June},}

%% ISCA 2015

@INPROCEEDINGS{hauswald2015djinnandtonic, 
author={J. Hauswald and Y. Kang and M. A. Laurenzano and Q. Chen and C. Li and T. Mudge and R. G. Dreslinski and J. Mars and L. Tang}, 
booktitle={2015 ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA)}, 
title={DjiNN and Tonic: DNN as a service and its implications for future warehouse scale computers}, 
year={2015}, 
volume={}, 
number={}, 
pages={27-40}, 
keywords={graphics processing units;learning (artificial intelligence);neural nets;DjiNN;NVIDIA K40 GPU;Tonic suite;WSC architectures;deep neural networks;homogeneous integrated GPU servers;machine learning;warehouse scale computers;Face;Graphics processing units;Libraries;Neural networks;Neurons;Servers;Throughput}, 
doi={10.1145/2749469.2749472}, 
ISSN={1063-6897}, 
month={June},}

@inproceedings{du2015shidiannao, 
    author={Z. Du and R. Fasthuber and T. Chen and P. Ienne and L. Li and T. Luo and X. Feng and Y. Chen and O. Temam}, 
    booktitle={ISCA},
    title={ShiDianNao: Shifting vision processing closer to the sensor}, 
    year={2015}, 
    month={June},
}

%% ISCA 2014

@inproceedings{putnam2014reconfigurable,
  title={A reconfigurable fabric for accelerating large-scale datacenter services},
  author={Putnam, Andrew and Caulfield, Adrian M and Chung, Eric S and Chiou, Derek and Constantinides, Kypros and Demme, John and Esmaeilzadeh, Hadi and Fowers, Jeremy and Gopal, Gopi Prashanth and Gray, Jan and others},
  booktitle={Computer Architecture (ISCA), 2014 ACM/IEEE 41st International Symposium on},
  pages={13--24},
  year={2014},
  organization={IEEE}
}

%% FPGA 2015

@inproceedings{zhang2015optimizing,
  title={Optimizing fpga-based accelerator design for deep convolutional neural networks},
  author={Zhang, Chen and Li, Peng and Sun, Guangyu and Guan, Yijin and Xiao, Bingjun and Cong, Jason},
  booktitle={Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays},
  pages={161--170},
  year={2015},
  organization={ACM}
}

%% Other

@article{ovtcharov2015accelerating,
  title={Accelerating deep convolutional neural networks using specialized hardware},
  author={Ovtcharov, Kalin and Ruwase, Olatunji and Kim, Joo-Young and Fowers, Jeremy and Strauss, Karin and Chung, Eric S},
  journal={Microsoft Research Whitepaper},
  volume={2},
  number={11},
  year={2015}
}

@inproceedings{lee2009convolutional,
  title={Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations},
  author={Lee, Honglak and Grosse, Roger and Ranganath, Rajesh and Ng, Andrew Y},
  booktitle={ICML},
  year={2009},
  organization={ACM}
}

@misc{karpathy2015cs231n, 
  title={CS231n Convolutional Neural Networks for Visual Recognition},
  author={Karpathy, Andrej},
  year={2015},
  url="\url{http://cs231n.github.io/convolutional-networks/}",
}

@inproceedings{vanhoucke2011improving,
  title={Improving the speed of neural networks on CPUs},
  author={Vanhoucke, Vincent and Senior, Andrew and Mao, Mark Z},
  booktitle={Proc. Deep Learning and Unsupervised Feature Learning NIPS Workshop},
  volume={1},
  pages={4},
  year={2011}
}

% HTM Hardware Accelerators

@inproceedings{walter2017spinnaker,
    author={F. Walter and M. Sandner and F. Rcöhrbein and A. Knoll}, 
    booktitle={ISCAS},
    title={Towards a neuromorphic implementation of hierarchical temporal memory on SpiNNaker}, 
    year={2017}, 
    month={May},
}

@inproceedings{gabrielsson2012hierarchical, 
    author={P. Gabrielsson and R. König and U. Johansson}, 
    booktitle={CIFEr}, 
    title={Hierarchical Temporal Memory-based algorithmic trading of financial markets}, 
    year={2012}, 
    month={March},
}

@article{sinkevicius2011monitoring,
  title={Monitoring of humans traffic using hierarchical temporal memory algorithms},
  author={Sinkevicius, S and Simutis, R and Raudonis, V},
  journal={Elektronika ir Elektrotechnika},
  volume={115},
  number={9},
  pages={91--96},
  year={2011}
}

@inproceedings{zhituo2012content,
  title={A Content-Based Image Retrieval System Using Multiple Hierarchical Temporal Memory Classifiers},
  author={Zhituo, Xia and Hao, Ruan and Hao, Wang},
  booktitle={ISCID},
  volume={2},
  pages={438--441},
  year={2012},
  organization={IEEE}
}

@article{bobier2007handwritten,
  title={Handwritten digit recognition using hierarchical temporal memory},
  author={Bobier, Bruce},
  journal={University of Guelph},
  year={2007}
}

% CONVs take 90% of cycles in CNN

%% Yangqing's Dissertation

@book{jia2014learning,
  title={Learning semantic image representations at a large scale},
  author={Jia, Yangqing},
  year={2014},
  publisher={University of California, Berkeley}
}

@inproceedings{ren2015vectorization,
  title={On Vectorization of Deep Convolutional Neural Networks for Vision Tasks.},
  author={Ren, Jimmy SJ and Xu, Li},
  booktitle={AAAI},
  pages={1840--1846},
  year={2015}
}

% Other

@article{fridman2017autonomous,
  title={MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation},
  author={Fridman, Lex and Brown, Daniel E and Glazer, Michael and Angell, William and Dodd, Spencer and Jenik, Benedikt and Terwilliger, Jack and Kindelsberger, Julia and Ding, Li and Seaman, Sean and others},
  journal={arXiv preprint arXiv:1711.06976},
  year={2017}
}

@article{laughlin2003communication,
  title={Communication in neuronal networks},
  author={Laughlin, Simon B and Sejnowski, Terrence J},
  journal={Science},
  volume={301},
  number={5641},
  pages={1870--1874},
  year={2003},
  publisher={American Association for the Advancement of Science}
}

@techreport{rosenblatt_principles_1961,
	title = {Principles of neurodynamics. perceptrons and the theory of brain mechanisms},
	institution = {CORNELL AERONAUTICAL LAB INC BUFFALO NY},
	author = {Rosenblatt, Frank},
	year = {1961},
	keywords = {multilayer perceptrons}
}

@inproceedings{dhawan2012fathom,
  title={Fathom: A browser-based network measurement platform},
  author={Dhawan, Mohan and Samuel, Justin and Teixeira, Renata and Kreibich, Christian and Allman, Mark and Weaver, Nicholas and Paxson, Vern},
  booktitle={Proceedings of the 2012 ACM conference on Internet measurement conference},
  pages={73--86},
  year={2012},
  organization={ACM}
}

@article{dlugosch2014efficient,
  title={An efficient and scalable semiconductor architecture for parallel automata processing},
  author={Dlugosch, Paul and Brown, Dave and Glendenning, Paul and Leventhal, Michael and Noyes, Harold},
  journal={IEEE Transactions on Parallel and Distributed Systems},
  volume={25},
  number={12},
  pages={3088--3098},
  year={2014},
  publisher={IEEE}
}

@techreport{rosenblatt1961principles,
  title={Principles of neurodynamics. perceptrons and the theory of brain mechanisms},
  author={Rosenblatt, Frank},
  year={1961},
  institution={CORNELL AERONAUTICAL LAB INC BUFFALO NY}
}

@article{ahmad2015properties,
  title={Properties of sparse distributed representations and their application to hierarchical temporal memory},
  author={Ahmad, Subutai and Hawkins, Jeff},
  journal={arXiv preprint arXiv:1503.07469},
  year={2015}
}

@article{mnatzaganian2017mathematical,
  title={A mathematical formalization of hierarchical temporal memory’s spatial pooler},
  author={Mnatzaganian, James and Fokou{\'e}, Ernest and Kudithipudi, Dhireesha},
  journal={Frontiers in Robotics and AI},
  volume={3},
  pages={81},
  year={2017},
  publisher={Frontiers}
}

@article{mcculloch1943logical,
  title={A logical calculus of the ideas immanent in nervous activity},
  author={McCulloch, Warren S and Pitts, Walter},
  journal={The bulletin of mathematical biophysics},
  volume={5},
  number={4},
  pages={115--133},
  year={1943},
  publisher={Springer}
}

@book{hebb2005organization,
  title={The organization of behavior: A neuropsychological theory},
  author={Hebb, Donald Olding},
  year={2005},
  publisher={Psychology Press}
}

@article{farley1954simulation,
  title={Simulation of self-organizing systems by digital computer},
  author={Farley, BWAC and Clark, W},
  journal={Transactions of the IRE Professional Group on Information Theory},
  volume={4},
  number={4},
  pages={76--84},
  year={1954},
  publisher={IEEE}
}

@article{rochester1956tests,
  title={Tests on a cell assembly theory of the action of the brain, using a large digital computer},
  author={Rochester, Nathaniel and Holland, J and Haibt, L and Duda, W},
  journal={IRE Transactions on information Theory},
  volume={2},
  number={3},
  pages={80--93},
  year={1956},
  publisher={IEEE}
}

@article{lecun1999object,
  title={Object recognition with gradient-based learning},
  author={LeCun, Yann and Haffner, Patrick and Bottou, L{\'e}on and Bengio, Yoshua},
  journal={Shape, contour and grouping in computer vision},
  pages={823--823},
  year={1999},
  publisher={Springer}
}

@article{economist2010data,
	title = {Data, data everywhere},
	issn = {0013-0613},
	url = {http://www.economist.com/node/15557443},
	abstract = {Information has gone from scarce to superabundant. That brings huge new benefits, says Kenneth Cukier (interviewed here)—but also big headaches},
	urldate = {2018-02-05},
	journal = {The Economist},
	month = feb,
	year = {2010},
	file = {The Economist Snapshot:/Users/matejaputic/Zotero/storage/S8U97HWK/15557443.html:text/html}
}

@inproceedings{moons2016energy,
  title={Energy-efficient convnets through approximate computing},
  author={Moons, Bert and De Brabandere, Bert and Van Gool, Luc and Verhelst, Marian},
  booktitle={Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on},
  pages={1--8},
  year={2016},
  organization={IEEE}
}

@inproceedings{moons2017dvafs,
  title={DVAFS: Trading computational accuracy for energy through dynamic-voltage-accuracy-frequency-scaling},
  author={Moons, Bert and Uytterhoeven, Roel and Dehaene, Wim and Verhelst, Marian},
  booktitle={2017 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)},
  pages={488--493},
  year={2017},
  organization={IEEE}
}

@inproceedings{kung2015power,
  title={A power-aware digital feedforward neural network platform with backpropagation driven approximate synapses},
  author={Kung, Jaeha and Kim, Duckhwan and Mukhopadhyay, Saibal},
  booktitle={Low Power Electronics and Design (ISLPED), 2015 IEEE/ACM International Symposium on},
  pages={85--90},
  year={2015},
  organization={IEEE}
}

@misc{kumar2013introducing,
  title={Introducing qualcomm zeroth processors: Brain-inspired computing},
  author={Kumar, Samir},
  year={2013},
  publisher={Qualcomm}
}

@inproceedings{preissl2012compass,
  title={Compass: A scalable simulator for an architecture for cognitive computing},
  author={Preissl, Robert and Wong, Theodore M and Datta, Pallab and Flickner, Myron and Singh, Raghavendra and Esser, Steven K and Risk, William P and Simon, Horst D and Modha, Dharmendra S},
  booktitle={Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis},
  pages={54},
  year={2012},
  organization={IEEE Computer Society Press}
}

@article{painkras2013spinnaker,
  title={SpiNNaker: A 1-W 18-core system-on-chip for massively-parallel neural network simulation},
  author={Painkras, Eustace and Plana, Luis A and Garside, Jim and Temple, Steve and Galluppi, Francesco and Patterson, Cameron and Lester, David R and Brown, Andrew D and Furber, Steve B},
  journal={IEEE Journal of Solid-State Circuits},
  volume={48},
  number={8},
  pages={1943--1953},
  year={2013},
  publisher={IEEE}
}

@article{merolla2014million,
  title={A million spiking-neuron integrated circuit with a scalable communication network and interface},
  author={Merolla, Paul A and Arthur, John V and Alvarez-Icaza, Rodrigo and Cassidy, Andrew S and Sawada, Jun and Akopyan, Filipp and Jackson, Bryan L and Imam, Nabil and Guo, Chen and Nakamura, Yutaka and others},
  journal={Science},
  volume={345},
  number={6197},
  pages={668--673},
  year={2014},
  publisher={American Association for the Advancement of Science}
}

@ARTICLE{BillaudelleAhmad,
    author = {Sebastian Billaudelle and Subutai Ahmad},
    title = {Porting HTM Models to the Heidelberg Neuromorphic Computing Platform},
    journal = {eprint arXiv:1505.02142},
    year = 2015,
    month = 5,
}

@mastersthesis{EpiphanyThesis,
  author       = {Xi Zhou and Yaoyao Luo},
  title        = {Implementation of Hierarchical Temporal Memory on a Many-Core Architecture},
  school       = {Halmstad University},
  year         = 2012,
  month        = 12,
}

@mastersthesis{FPGAThesis,
  author       = {Abdullah M. Zyarah},
  title        = {Design and Analysis of a Reconfigurable Hierarchical Temporal Memory Architecture},
  school       = {RIT},
  year         = 2015,
}

@mastersthesis{InspiredThesis,
  author       = {Mandar Deshpande},
  title        = {FPGA Implementation and Acceleration of Building blocks for Biologically Inspired Computational Models},
  school       = {PDX},
  year         = 2011,
}

@inproceedings{zhang2015approxann,
  title={ApproxANN: an approximate computing framework for artificial neural network},
  author={Zhang, Qian and Wang, Ting and Tian, Ye and Yuan, Feng and Xu, Qiang},
  booktitle={Proceedings of the 2015 Design, Automation \& Test in Europe Conference \& Exhibition},
  pages={701--706},
  year={2015},
  organization={EDA Consortium}
}

@inproceedings{venkataramani2014axnn,
  title={AxNN: energy-efficient neuromorphic systems using approximate computing},
  author={Venkataramani, Swagath and Ranjan, Ashish and Roy, Kaushik and Raghunathan, Anand},
  booktitle={Proceedings of the 2014 international symposium on Low power electronics and design},
  pages={27--32},
  year={2014},
  organization={ACM}
}

@article{du2015leveraging,
  title={Leveraging the error resilience of neural networks for designing highly energy efficient accelerators},
  author={Du, Zidong and Lingamneni, Avinash and Chen, Yunji and Palem, Krishna V and Temam, Olivier and Wu, Chengyong},
  journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
  volume={34},
  number={8},
  pages={1223--1235},
  year={2015},
  publisher={IEEE}
}

@article{yasoubi2017power,
  title={Power-efficient accelerator design for neural networks using computation reuse},
  author={Yasoubi, Ali and Hojabr, Reza and Modarressi, Mehdi},
  journal={IEEE Computer Architecture Letters},
  volume={16},
  number={1},
  pages={72--75},
  year={2017},
  publisher={IEEE}
}

@inproceedings{han2015learning,
  title={Learning both weights and connections for efficient neural network},
  author={Han, Song and Pool, Jeff and Tran, John and Dally, William},
  booktitle={Advances in Neural Information Processing Systems},
  pages={1135--1143},
  year={2015}
}

@inproceedings{misailovic2014chisel,
  title={Chisel: Reliability-and accuracy-aware optimization of approximate computational kernels},
  author={Misailovic, Sasa and Carbin, Michael and Achour, Sara and Qi, Zichao and Rinard, Martin C},
  booktitle={ACM SIGPLAN Notices},
  volume={49},
  number={10},
  pages={309--328},
  year={2014},
  organization={ACM}
}

@inproceedings{esmaeilzadeh2012neural,
  title={Neural acceleration for general-purpose approximate programs},
  author={Esmaeilzadeh, Hadi and Sampson, Adrian and Ceze, Luis and Burger, Doug},
  booktitle={Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture},
  pages={449--460},
  year={2012},
  organization={IEEE Computer Society}
}

@inproceedings{venkataramani2013quality,
  title={Quality programmable vector processors for approximate computing},
  author={Venkataramani, Swagath and Chippa, Vinay K and Chakradhar, Srimat T and Roy, Kaushik and Raghunathan, Anand},
  booktitle={Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture},
  pages={1--12},
  year={2013},
  organization={ACM}
}

@article{judd2015reduced,
  title={Reduced-precision strategies for bounded memory in deep neural nets},
  author={Judd, Patrick and Albericio, Jorge and Hetherington, Tayler and Aamodt, Tor and Jerger, Natalie Enright and Urtasun, Raquel and Moshovos, Andreas},
  journal={arXiv preprint arXiv:1511.05236},
  year={2015}
}

@inproceedings{mazahir2016area,
  title={An area-efficient consolidated configurable error correction for approximate hardware accelerators},
  author={Mazahir, Sana and Hasan, Osman and Hafiz, Rehan and Shafique, Muhammad and Henkel, J{\"o}rg},
  booktitle={Design Automation Conference (DAC), 2016 53nd ACM/EDAC/IEEE},
  pages={1--6},
  year={2016},
  organization={IEEE}
}

@book{mohapatra2011approximate,
  title={Approximate computing: Enabling voltage over-scaling in multimedia applications},
  author={Mohapatra, Debabrata},
  year={2011},
  publisher={Purdue University}
}

@inproceedings{gupta2011impact,
  title={IMPACT: imprecise adders for low-power approximate computing},
  author={Gupta, Vaibhav and Mohapatra, Debabrata and Park, Sang Phill and Raghunathan, Anand and Roy, Kaushik},
  booktitle={Proceedings of the 17th IEEE/ACM international symposium on Low-power electronics and design},
  pages={409--414},
  year={2011},
  organization={IEEE Press}
}

@inproceedings{chippa2014storm,
  title={StoRM: a stochastic recognition and mining processor},
  author={Chippa, Vinay K and Venkataramani, Swagath and Roy, Kaushik and Raghunathan, Anand},
  booktitle={Proceedings of the 2014 international symposium on Low power electronics and design},
  pages={39--44},
  year={2014},
  organization={ACM}
}

@inproceedings{eldridge2014neural,
  title={Neural network-based accelerators for transcendental function approximation},
  author={Eldridge, Schuyler and Raudies, Florian and Zou, David and Joshi, Ajay},
  booktitle={Proceedings of the 24th edition of the great lakes symposium on VLSI},
  pages={169--174},
  year={2014},
  organization={ACM}
}

@inproceedings{jiao2017assessment,
  title={An Assessment of Vulnerability of Hardware Neural Networks to Dynamic Voltage and Temperature Variations},
  author={Jiao, Xun and Luo, Mulong and Lin, Jeng-Hau and Gupta, Rajesh K},
  booktitle={IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Irvine, USA},
  year={2017}
}

@article{zhang2017enabling,
  title={Enabling Extreme Energy Efficiency Via Timing Speculation for Deep Neural Network Accelerators},
  author={Zhang, Jeff and Ghodsi, Zahra and Rangineni, Kartheek and Garg, Siddharth},
  year={2017}
}

@inproceedings{li2017understanding,
  title={Understanding error propagation in deep learning neural network (DNN) accelerators and applications},
  author={Li, Guanpeng and Hari, Siva Kumar Sastry and Sullivan, Michael and Tsai, Timothy and Pattabiraman, Karthik and Emer, Joel and Keckler, Stephen W},
  booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
  pages={8},
  year={2017},
  organization={ACM}
}

@inproceedings{ernst2003razor,
  title={Razor: A low-power pipeline based on circuit-level timing speculation},
  author={Ernst, Dan and Kim, Nam Sung and Das, Shidhartha and Pant, Sanjay and Rao, Rajeev and Pham, Toan and Ziesler, Conrad and Blaauw, David and Austin, Todd and Flautner, Krisztian and others},
  booktitle={Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture},
  pages={7},
  year={2003},
  organization={IEEE Computer Society}
}

@article{wadden2016vasim,
  title={VASim: An open virtual automata simulator for automata processing application and architecture research},
  author={Wadden, Jack and Skadron, Kevin},
  journal={Tech. Rep. CS2016--03, University of Virginia},
  year={2016}
}

@unpublished{dean2017nips,
  title={Machine Learning for Systems and Systems for Machine Learning},
  author={Dean, Jeff},
  year={2017},
  note={Advances in Neural Information Processing Systems 30}
}


@misc{wikipedia2018neuroplasticity,
	title = {Neuroplasticity},
	copyright = {Creative Commons Attribution-ShareAlike License},
	url = {https://en.wikipedia.org/w/index.php?title=Neuroplasticity&oldid=825984492},
	language = {en},
	urldate = {2018-04-11},
	journal = {Wikipedia},
	month = feb,
	year = {2018},
	note = {Page Version ID: 825984492},
}

@techreport{hawkins2006hierarchical,
  title={Hierarchical temporal memory: Concepts, theory and terminology},
  author={Hawkins, Jeff and George, Dileep},
  year={2006},
  institution={Technical report, Numenta}
}

@article{hawkins2010hierarchical,
  title={Hierarchical temporal memory including HTM cortical learning algorithms},
  author={Hawkins, Jeff and Ahmad, Subutai and Dubinsky, Donna},
  journal={Techical report, Numenta, Inc, Palto Alto http://www. numenta. com/htmoverview/education/HTM\_CorticalLearningAlgorithms. pdf},
  year={2010}
}
@inproceedings{doremalen2008spoken,
  title={Spoken digit recognition using a hierarchical temporal memory},
  author={Doremalen, Joost van and Boves, Lou},
  booktitle={Ninth Annual Conference of the International Speech Communication Association},
  year={2008}
}

@article{fan2016hierarchical,
  title={Hierarchical temporal memory based on spin-neurons and resistive memory for energy-efficient brain-inspired computing},
  author={Fan, Deliang and Sharad, Mrigank and Sengupta, Abhronil and Roy, Kaushik},
  journal={IEEE transactions on neural networks and learning systems},
  volume={27},
  number={9},
  pages={1907--1919},
  year={2016},
  publisher={IEEE}
}

@inproceedings{lavin2015evaluating,
  title={Evaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark},
  author={Lavin, Alexander and Ahmad, Subutai},
  booktitle={Machine Learning and Applications (ICMLA), 2015 IEEE 14th International Conference on},
  pages={38--44},
  year={2015},
  organization={IEEE}
}

@article{ahmad2016real,
  title={Real-time anomaly detection for streaming analytics},
  author={Ahmad, Subutai and Purdy, Scott},
  journal={arXiv preprint arXiv:1607.02480},
  year={2016}
}

@techreport{bonhoff2008using,
  title={Using Hierarchical Temporal Memory for Detecting Anomalous Network Activity},
  author={Bonhoff, Gerod M},
  year={2008},
  institution={AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT}
}

@article{putic2017hierarchical,
  title={Hierarchical Temporal Memory on the Automata Processor},
  author={Putic, Mateja and Varshneya, AJ and Stan, Mircea R},
  journal={IEEE Micro},
  volume={37},
  number={1},
  pages={52--59},
  year={2017},
  publisher={IEEE}
}

@inproceedings{ganapathy2017characterizing,
  title={On characterizing near-threshold SRAM failures in FinFET technology},
  author={Ganapathy, Shrikanth and Kalamatianos, John and Kasprak, Keith and Raasch, Steven},
  booktitle={Proceedings of the 54th Annual Design Automation Conference 2017},
  pages={53},
  year={2017},
  organization={ACM}
}

@article{mitra2005robust,
  title={Robust system design with built-in soft-error resilience},
  author={Mitra, Subhasish and Seifert, Norbert and Zhang, Ming and Shi, Quan and Kim, Kee Sup},
  journal={Computer},
  volume={38},
  number={2},
  pages={43--52},
  year={2005},
  publisher={IEEE}
}

@inproceedings{blome2006cost,
  title={Cost-efficient soft error protection for embedded microprocessors},
  author={Blome, Jason A and Gupta, Shantanu and Feng, Shuguang and Mahlke, Scott},
  booktitle={Proceedings of the 2006 international conference on Compilers, architecture and synthesis for embedded systems},
  pages={421--431},
  year={2006},
  organization={ACM}
}

@article{cheng2017tolerating,
  title={Tolerating soft errors in processor cores using CLEAR (cross-layer exploration for architecting resilience)},
  author={Cheng, Eric and Mirkhani, Shahrzad and Szafaryn, Lukasz G and Cher, Chen-Yong and Cho, Hyungmin and Skadron, Kevin and Stan, Mircea R and Lilja, Klas and Abraham, Jacob A and Bose, Pradip and others},
  journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
  year={2017},
  publisher={IEEE}
}

@book{keckler2009multicore,
  title={Multicore processors and systems},
  author={Keckler, Stephen W and Hofstee, H Peter and Olukotun, Kunle},
  year={2009},
  publisher={Springer}
}

@inproceedings{parashar2013triggered,
  title={Triggered instructions: a control paradigm for spatially-programmed architectures},
  author={Parashar, Angshuman and Pellauer, Michael and Adler, Michael and Ahsan, Bushra and Crago, Neal and Lustig, Daniel and Pavlov, Vladimir and Zhai, Antonia and Gambhir, Mohit and Jaleel, Aamer and others},
  booktitle={ACM SIGARCH Computer Architecture News},
  volume={41},
  number={3},
  pages={142--153},
  year={2013},
  organization={ACM}
}

@misc{parloff2016why,
	title = {Why Deep Learning Is Suddenly Changing Your Life},
	author = {Roger Parloff},
	url = {http://fortune.com/ai-artificial-intelligence-deep-machine-learning/},
	language = {en},
	urldate = {2018-04-21},
	journal = {Fortune},
}

@inproceedings{koster2017flexpoint,
  title={Flexpoint: An adaptive numerical format for efficient training of deep neural networks},
  author={K{\"o}ster, Urs and Webb, Tristan and Wang, Xin and Nassar, Marcel and Bansal, Arjun K and Constable, William and Elibol, Oguz and Gray, Scott and Hall, Stewart and Hornof, Luke and others},
  booktitle={Advances in Neural Information Processing Systems},
  pages={1742--1752},
  year={2017}
}

@article{snir2014addressing,
  title={Addressing failures in exascale computing},
  author={Snir, Marc and Wisniewski, Robert W and Abraham, Jacob A and Adve, Sarita V and Bagchi, Saurabh and Balaji, Pavan and Belak, Jim and Bose, Pradip and Cappello, Franck and Carlson, Bill and others},
  journal={The International Journal of High Performance Computing Applications},
  volume={28},
  number={2},
  pages={129--173},
  year={2014},
  publisher={Sage Publications Sage UK: London, England}
}

@misc{cheng2017maximum,
Author = {Chih-Hong Cheng and Georg Nührenberg and Harald Ruess},
Title = {Maximum Resilience of Artificial Neural Networks},
Year = {2017},
Eprint = {arXiv:1705.01040},
}

@misc{dodge2017quality,
Author = {Samuel Dodge and Lina Karam},
Title = {Quality Resilient Deep Neural Networks},
Year = {2017},
Eprint = {arXiv:1703.08119},
}

@misc{szegedy2013intriguiing,
Author = {Christian Szegedy and Wojciech Zaremba and Ilya Sutskever and Joan Bruna and Dumitru Erhan and Ian Goodfellow and Rob Fergus},
Title = {Intriguing properties of neural networks},
Year = {2013},
Eprint = {arXiv:1312.6199},
}

@misc{zhou2017classification,
Author = {Yiren Zhou and Sibo Song and Ngai-Man Cheung},
Title = {On Classification of Distorted Images with Deep Convolutional Neural Networks},
Year = {2017},
Eprint = {arXiv:1701.01924},
}

@article{srivastava2014dropout,
  title={Dropout: A simple way to prevent neural networks from overfitting},
  author={Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
  journal={The Journal of Machine Learning Research},
  volume={15},
  number={1},
  pages={1929--1958},
  year={2014},
  publisher={JMLR. org}
}

@article{lin2013network,
  title={Network in network},
  author={Lin, Min and Chen, Qiang and Yan, Shuicheng},
  journal={arXiv preprint arXiv:1312.4400},
  year={2013}
}

@inproceedings{denton2014exploiting,
  title={Exploiting linear structure within convolutional networks for efficient evaluation},
  author={Denton, Emily L and Zaremba, Wojciech and Bruna, Joan and LeCun, Yann and Fergus, Rob},
  booktitle={Advances in neural information processing systems},
  pages={1269--1277},
  year={2014}
}

@article{gong2014compressing,
  title={Compressing deep convolutional networks using vector quantization},
  author={Gong, Yunchao and Liu, Liu and Yang, Ming and Bourdev, Lubomir},
  journal={arXiv preprint arXiv:1412.6115},
  year={2014}
}

@inproceedings{du2014leveraging,
  title={Leveraging the error resilience of machine-learning applications for designing highly energy efficient accelerators},
  author={Du, Zidong and Lingamneni, Avinash and Chen, Yunji and Palem, Krishna and Temam, Olivier and Wu, Chengyong},
  booktitle={Design Automation Conference (ASP-DAC), 2014 19th Asia and South Pacific},
  pages={201--206},
  year={2014},
  organization={IEEE}
}

@inproceedings{sankaralingam2003exploiting,
  title={Exploiting ILP, TLP, and DLP with the polymorphous TRIPS architecture},
  author={Sankaralingam, Karthikeyan and Nagarajan, Ramadass and Liu, Haiming and Kim, Changkyu and Huh, Jaehyuk and Burger, Doug and Keckler, Stephen W and Moore, Charles R},
  booktitle={ACM SIGARCH Computer Architecture News},
  volume={31},
  number={2},
  pages={422--433},
  year={2003},
  organization={ACM}
}

@article{taylor2002raw,
  title={The raw microprocessor: A computational fabric for software circuits and general-purpose programs},
  author={Taylor, Michael Bedford and Kim, Jason and Miller, Jason and Wentzlaff, David and Ghodrat, Fae and Greenwald, Ben and Hoffman, Henry and Johnson, Paul and Lee, Jae-Wook and Lee, Walter and others},
  journal={IEEE micro},
  volume={22},
  number={2},
  pages={25--35},
  year={2002},
  publisher={IEEE}
}

@misc{molchanov2016pruning,
Author = {Pavlo Molchanov and Stephen Tyree and Tero Karras and Timo Aila and Jan Kautz},
Title = {Pruning Convolutional Neural Networks for Resource Efficient Inference},
Year = {2016},
Eprint = {arXiv:1611.06440},
}

@inproceedings{yu2017scalpel,
  title={Scalpel: Customizing dnn pruning to the underlying hardware parallelism},
  author={Yu, Jiecao and Lukefahr, Andrew and Palframan, David and Dasika, Ganesh and Das, Reetuparna and Mahlke, Scott},
  booktitle={Proceedings of the 44th Annual International Symposium on Computer Architecture},
  pages={548--560},
  year={2017},
  organization={ACM}
}


@misc{knowles2017designing,
	title = {Simon {Knowles}: {Designing} {Processors} for {Intelligence}},
	shorttitle = {Simon {Knowles}},
	url = {https://www.youtube.com/watch?v=7XtBZ4Hsi_M},
	urldate = {2018-04-22},
	author = {{UC Berkeley EECS Events}}
}

@article{kung1982wavefront,
  title={Wavefront array processor: Language, architecture, and applications},
  author={Kung, Sun-Yuan and Rao, Bhaskar and others},
  journal={IEEE Transactions on Computers},
  volume={100},
  number={11},
  pages={1054--1066},
  year={1982},
  publisher={IEEE}
}

@article{kung1984supercomputing,
  title={On supercomputing with systolic/wavefront array processors},
  author={Kung, Sun-Yuan},
  journal={Proceedings of the IEEE},
  volume={72},
  number={7},
  pages={867--884},
  year={1984},
  publisher={IEEE}
}


@article{nielsen2015neural,
	title = {Neural {Networks} and {Deep} {Learning}},
	url = {http://neuralnetworksanddeeplearning.com},
	language = {en},
	urldate = {2018-04-24},
	author = {Nielsen, Michael A.},
	year = {2015},
	file = {Snapshot:/Users/matejaputic/Zotero/storage/SCK67M2R/chap2.html:text/html}
}