lapnd/tinymlperf

A fork of TinyMLPerf modified to use our of TensorFlow Lite for Microcontrollers utilises a part of the RISC-V Vector Extension to improve performance.

tinyMLPerf Deep Learning Benchmarks for Embedded Devices

The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power.

TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings.

TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware.

CV32E40P Compilation

Setup

The following commands will install the dependencies for this repository

sudo apt install unzip curl

Building for CV32E40P Verilator Model

To build TinyMLPerf you must first navigate to the ./v0.1 directory.

cd ./v0.1

Build for Standard ISA CV32E40P

make TARGET_ARCH=cv32e40p                                                       # Build the ELF File
riscv32-unknown-elf-objcopy -O verilog ./vww_perf_runner vww_perf_runner.hex    # Convert to HEX
testbench_verilator "+firmware=./vww_perf_runner.hex"                           # Execute on Verilator Model

Build for ML Accelerated CV32E40P

make TARGET_ARCH=cv32e40p-ml                                                    # Build the ELF File
riscv32-unknown-elf-objcopy -O verilog ./vww_perf_runner vww_perf_runner.hex    # Convert to HEX
testbench_verilator "+firmware=./vww_perf_runner.hex"                           # Execute on Verilator Model

Building for Spike

To build TinyMLPerf you must first navigate to the ./v0.1 directory.

cd ./v0.1

Build for Standard ISA CV32E40P

make TARGET_ARCH=cv32e40p TAGS=spike                                                                        # Build the ELF File    
spike --isa=RV32IMCV --varch=vlen:32,elen:32,slen:32 /opt/riscv/riscv32-unknown-elf/bin/pk vww_perf_runner  # Execute on Spike set to mimic CV32E40P

Build for ML Accelerated CV32E40P

make TARGET_ARCH=cv32e40p-ml TAGS=spike                                                                     # Build the ELF File    
spike --isa=RV32IMCV --varch=vlen:32,elen:32,slen:32 /opt/riscv/riscv32-unknown-elf/bin/pk vww_perf_runner  # Execute on Spike set to mimic CV32E40P

Specify TensorFlow Version

These instructions allow a user to specify the version of TensorFlow on which TinyMLPerf is run.

1. Find the hash/sha of the commit you would like to use. This can be on any branch, not just master.
2. Open the file v0.1/example_submission/download_and_build_tfmicro.sh
3. Replace the value of the variable TF_COMMIT_SHA with the hash/sha you found in step 1.
4. run make clean from within the dir ./v0.1 and delete the directory v0.1/example_submission/tensorflow
   This will ensure the new version of TensorFlow will be downloaded when the next make command is run.