From 7b181d4a848adf78cdf9764ad387236d77cc6c6d Mon Sep 17 00:00:00 2001 From: Aba Date: Fri, 22 Nov 2024 11:40:59 -0800 Subject: [PATCH] Fix example & readme --- README.md | 300 ++++++++++++--------------------- deepsocflow/c/xilinx_example.c | 2 +- deepsocflow/py/dataflow.py | 15 +- docs/overview.png | Bin 0 -> 271757 bytes run/example.py | 238 ++++++++++++++------------ 5 files changed, 250 insertions(+), 305 deletions(-) create mode 100644 docs/overview.png diff --git a/README.md b/README.md index 1cae88d..04855d2 100644 --- a/README.md +++ b/README.md @@ -1,192 +1,108 @@ - - -# An Open Framework to Empower Scientific Edge Computing with Modern Neural Networks ![status](https://github.com/abarajithan11/dnn-engine/actions/workflows/verify.yml/badge.svg) - -cgra4ml is a Python library that helps researchers build, train, and implement their own deep ML models, such as ResNet CNNs, Autoencoders, and Transformers on FPGAs and custom ASIC. - -It takes a lot of effort and expertise to implement highly optimized neural networks on edge platforms. The challenging aspects include: - -- Designing an optimal dataflow architecture -- Building & verifying an accelerator, optimizing for high-frequency -- Building the System-on-Chip, verifying and optimizing data bottlenecks -- Writing C firmware to control the accelerator and verify its correctness - -Often, after all that work, the models do not meet their expected performance due to memory bottlenecks and sub-optimal hardware implementation. - -We present a highly flexible, high-performance accelerator system that can be adjusted to your needs through a simple Python API. The framework is maintained as open source, allowing a user to modify the processing element to their desired data type using customized architecture, easily expand the architecture to meet the desired performance, and implement new neural network models. - -

- -## User API - -![System](docs/workflow.png) - -```py -from deepsocflow import Bundle, Hardware, QModel, QInput - -''' -0. Specify Hardware -''' -hw = Hardware ( # Alternatively: hw = Hardware.from_json('hardware.json') - processing_elements = (8, 96) , # (rows, columns) of multiply-add units - frequency_mhz = 250 , # - bits_input = 4 , # bit width of input pixels and activations - bits_weights = 4 , # bit width of weights - bits_sum = 16 , # bit width of accumulator - bits_bias = 16 , # bit width of bias - max_batch_size = 64 , # - max_channels_in = 2048 , # - max_kernel_size = 13 , # - max_image_size = 512 , # - ram_weights_depth = 20 , # - ram_edges_depth = 288 , # - axi_width = 64 , # - target_cpu_int_bits = 32 , # - valid_prob = 0.1 , # probability in which AXI-Stream s_valid signal should be toggled in simulation - ready_prob = 0.1 , # probability in which AXI-Stream m_ready signal should be toggled in simulation - data_dir = 'vectors', # directory to store generated test vectors - ) -hw.export() # Generates: config_hw.svh, config_hw.tcl, config_tb.svh, hardware.json -hw.export_vivado_tcl(board='zcu104') - - -''' -1. Build Model -''' -XN = 1 -input_shape = (XN,18,18,3) # (XN, XH, XW, CI) - -QINT_BITS = 0 -kq = f'quantized_bits({hw.K_BITS},{QINT_BITS},False,True,1)' -bq = f'quantized_bits({hw.B_BITS},{QINT_BITS},False,True,1)' -q1 = f'quantized_relu({hw.X_BITS},{QINT_BITS},negative_slope=0)' -q2 = f'quantized_bits({hw.X_BITS},{QINT_BITS},False,False,1)' -q3 = f'quantized_bits({hw.X_BITS},{QINT_BITS},False,True,1)' -q4 = f'quantized_relu({hw.X_BITS},{QINT_BITS},negative_slope=0.125)' - -x = x_in = QInput(shape=input_shape[1:], batch_size=XN, hw=hw, int_bits=QINT_BITS, name='input') - -x = x_skip1 = Bundle( core= {'type':'conv' , 'filters':8 , 'kernel_size':(11,11), 'strides':(2,1), 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':True , 'act_str':q1}, pool= {'type':'avg', 'size':(3,4), 'strides':(2,3), 'padding':'same', 'act_str':f'quantized_bits({hw.X_BITS},0,False,False,1)'})(x) -x = x_skip2 = Bundle( core= {'type':'conv' , 'filters':8 , 'kernel_size':( 1, 1), 'strides':(1,1), 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':True , 'act_str':q2}, add = {'act_str':f'quantized_bits({hw.X_BITS},0,False,True,1)'})(x, x_skip1) -x = Bundle( core= {'type':'conv' , 'filters':8 , 'kernel_size':( 7, 7), 'strides':(1,1), 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':False, 'act_str':q3}, add = {'act_str':f'quantized_bits({hw.X_BITS},0,False,True,1)'})(x, x_skip2) -x = Bundle( core= {'type':'conv' , 'filters':8 , 'kernel_size':( 5, 5), 'strides':(1,1), 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':True , 'act_str':q4}, add = {'act_str':f'quantized_bits({hw.X_BITS},0,False,True,1)'})(x, x_skip1) -x = Bundle( core= {'type':'conv' , 'filters':24, 'kernel_size':( 3, 3), 'strides':(1,1), 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':True , 'act_str':q1},)(x) -x = Bundle( core= {'type':'conv' , 'filters':10, 'kernel_size':( 1, 1), 'strides':(1,1), 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':True , 'act_str':q4}, flatten= True)(x) -x = Bundle( core= {'type':'dense', 'units' :10, 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':True , 'act_str':q4}, softmax= True)(x) - -model = QModel(inputs=x_in.raw, outputs=x) -model.compile() -model.summary() - -''' -2. TRAIN (using qkeras) -''' -# model.fit(...) - - -''' -3. EXPORT FOR INFERENCE -''' -SIM, SIM_PATH = 'xsim', "F:/Xilinx/Vivado/2022.1/bin/" # For Xilinx Vivado -# SIM, SIM_PATH = 'verilator', "" # For Verilator - -model.export_inference(x=model.random_input, hw=hw) # Runs forward pass in float & int, compares them. Generates: config_fw.h (C firmware), weights.bin, expected.bin -model.verify_inference(SIM=SIM, SIM_PATH=SIM_PATH) # Runs SystemVerilog testbench with the model & weights, randomizing handshakes, testing with actual C firmware in simulation - -''' -4. IMPLEMENTATION - -a. FPGA: Open vivado, source vivado_flow.tcl -b. ASIC: Set PDK paths, run syn.tcl & pnr.tcl -c. Compile C firmware with generated header (config_fw.h) and run on device -''' -``` - -## Execution API -```c -#define NDEBUG -#include "platform.h" -#include "deepsocflow_xilinx.h" - -int main() { - - hardware_setup(); - xil_printf("Welcome to DeepSoCFlow!\n Store weights, biases & inputs at: %p; \n", &mem.w); - - model_setup(); - model_run(); // run model and measure time - - // Print: outputs & measured time - Xil_DCacheFlushRange((INTPTR)&mem.y, sizeof(mem.y)); // force transfer to DDR, starting addr & length - for (int i=0; i Properties - - ARM v8 gcc compiler -> Directories -> Add Include Paths: Add absolute paths of `run/work` and `deepsocflow/c` - - ARM v8 gcc compiler -> Optimization -> Optimization most (-O3) - - ARM v8 gcc linker -> Libraries -> Add Library: `m` (math library) -- Build, Connect board & launch debug -- Add a breakpoint at `model_setup()`. When breakpoint hits, load `run/work/vectors/wbx.bin` to the address printed. -- Continue - This will run the model and print outputs & execution time - -4. ASIC implementation with Cadence Genus & Innovus: -```bash -# First add your PDK to 'asic/pdk', change paths in the scripts and run: -cd run/work -genus -f ../../tcl/asic/run_genus.tcl -innovus -source ../../tcl/asic/pnr.tcl -``` - -## Framework Infrastructure - -

- - -## Team Members - -- Aba -- Zhenghua + + +# CGRA4ML: A Framework to Implement Modern Neural Networks for Scientific Edge Computing ![status](https://github.com/abarajithan11/dnn-engine/actions/workflows/verify.yml/badge.svg) + +cgra4ml is a Python library that helps researchers build, train, and implement their own deep ML models, such as ResNet CNNs, Autoencoders, and Transformers on FPGAs and custom ASIC. + +It takes a lot of effort and expertise to implement highly optimized neural networks on edge platforms. The challenging aspects include: + +- Designing an optimal dataflow architecture +- Building & verifying an accelerator, optimizing for high-frequency +- Building the System-on-Chip, verifying and optimizing data bottlenecks +- Writing C firmware to control the accelerator and verify its correctness + +Often, after all that work, the models do not meet their expected performance due to memory bottlenecks and sub-optimal hardware implementation. + +We present a highly flexible, high-performance accelerator system that can be adjusted to your needs through a simple Python API. The framework is maintained as open source, allowing a user to modify the processing element to their desired data type using customized architecture, easily expand the architecture to meet the desired performance, and implement new neural network models. + +

+ + +## Execution API +```c +#define NDEBUG +#include "platform.h" +#include "deepsocflow_xilinx.h" + +int main() { + + hardware_setup(); + xil_printf("Welcome to DeepSoCFlow!\n Store weights, biases & inputs at: %p; \n", &mem.w); + + model_setup(); + model_run(); // run model and measure time + + // Print: outputs & measured time + Xil_DCacheFlushRange((INTPTR)&mem.y, sizeof(mem.y)); // force transfer to DDR, starting addr & length + for (int i=0; i Properties + - ARM v8 gcc compiler -> Directories -> Add Include Paths: Add absolute paths of `run/work` and `deepsocflow/c` + - ARM v8 gcc compiler -> Optimization -> Optimization most (-O3) + - ARM v8 gcc linker -> Libraries -> Add Library: `m` (math library) +- Build, Connect board & launch debug +- Add a breakpoint at `model_setup()`. When breakpoint hits, load `run/work/vectors/wbx.bin` to the address printed. +- Continue - This will run the model and print outputs & execution time + +4. ASIC implementation with Cadence Genus & Innovus: +```bash +# First add your PDK to 'asic/pdk', change paths in the scripts and run: +cd run/work +genus -f ../../tcl/asic/run_genus.tcl +innovus +source ../../tcl/asic/pnr.tcl +``` + +## Framework Infrastructure + +

+ + +## Team Members + +- Aba +- Zhenghua diff --git a/deepsocflow/c/xilinx_example.c b/deepsocflow/c/xilinx_example.c index 1913565..9a08fe9 100644 --- a/deepsocflow/c/xilinx_example.c +++ b/deepsocflow/c/xilinx_example.c @@ -13,7 +13,7 @@ int main() // Memory_st *p_mem = (Memory_st *)mmap(NULL, sizeof(Memory_st), PROT_READ | PROT_WRITE, MAP_SHARED, dh, MEM_BASEADDR); // void *p_config = mmap(NULL, 4*16+N_BUNDLES*32, PROT_READ | PROT_WRITE, MAP_SHARED, dh, CONFIG_BASEADDR); - xil_printf("Welcome to DeepSoCFlow!\n Store wbx at: %p; y:%p; buffers {0:%p,1:%p};\n", &p_mem->w, &mp->y, &p_mem->out_buffers[0], &p_mem->out_buffers[1]); + xil_printf("Welcome to DeepSoCFlow!\n Store wbx at: %p; y:%p; buffers {0:%p,1:%p};\n", &p_mem->w, &p_mem->y, &p_mem->out_buffers[0], &p_mem->out_buffers[1]); model_setup(p_mem, p_config); model_run_timed(p_mem, p_config, 20); // run model and measure time diff --git a/deepsocflow/py/dataflow.py b/deepsocflow/py/dataflow.py index 61c5799..ab3034d 100644 --- a/deepsocflow/py/dataflow.py +++ b/deepsocflow/py/dataflow.py @@ -313,28 +313,29 @@ def predict_bundle_performance(hw, r): utilization = operations / (hw.ROWS * hw.COLS * clocks) - return clocks, mem_bits, utilization + return clocks, mem_bits, utilization, operations def predict_model_performance(hw): d_out = { - 'clocks_total': 0, - 'mem_bytes_total': 0, + 'operations': [], 'utilization_all': [], 'clocks_all': [], 'mem_bytes_all': [], } for b in BUNDLES: - clocks, mem_bits, utilization = predict_bundle_performance(hw=hw, r=b.r) - d_out['clocks_total'] += clocks - d_out['mem_bytes_total'] += mem_bits/8 - + clocks, mem_bits, utilization, operations = predict_bundle_performance(hw=hw, r=b.r) + d_out['operations'] += [operations] d_out['utilization_all'] += [utilization] d_out['clocks_all'] += [clocks] d_out['mem_bytes_all'] += [mem_bits/8] print(f'---{b.ib}: util:{100*utilization:.2f} mem_mb:{mem_bits/1024**2:.2f} {b.r.XN=} {b.r.XH=} {b.r.XW=} {b.r.CI=} {b.r.CO=} {b.r.KH=} {b.r.KW=}') + 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