μTVM
Deep Learning on Bare-Metal Devices
Pratyush Patel
TVM Deep Learning on Bare-Metal Devices Pratyush Patel No OS - - PowerPoint PPT Presentation
TVM Deep Learning on Bare-Metal Devices Pratyush Patel No OS stack Extend TVM to support bare-metal devices Optimization High-Level Differentiable IR AutoTVM Tensor Expression IR LLVM, CUDA VTA AutoVTA Hardware FPGA ASIC Fleet
Pratyush Patel
Optimization AutoTVM AutoVTA High-Level Differentiable IR Tensor Expression IR VTA
FPGA ASIC
Hardware Fleet LLVM, CUDA
Optimization AutoTVM AutoVTA High-Level Differentiable IR Tensor Expression IR VTA
FPGA ASIC
Hardware Fleet LLVM, CUDA
Most bare-metal devices do not support LLVM
Optimization AutoTVM AutoVTA High-Level Differentiable IR Tensor Expression IR VTA
FPGA ASIC
Hardware Fleet LLVM, CUDA C, C++
Optimization AutoTVM AutoVTA High-Level Differentiable IR Tensor Expression IR VTA
FPGA ASIC
Hardware Fleet LLVM, CUDA C, C++
Upstreamed!
Optimization AutoTVM AutoVTA High-Level Differentiable IR Tensor Expression IR VTA
FPGA ASIC
Hardware Fleet LLVM, CUDA C, C++
Many other backends Upstreamed!
μTVM Runtime C Code Generator μDevice API run send program
μTVM Runtime C Code Generator μDevice API OpenOCD telnet JTAG run
μTVM Runtime C Code Generator μDevice API infer.c OpenOCD telnet JTAG IR->code run
μTVM Runtime C Code Generator μDevice API infer.c infer.o vendor gcc OpenOCD telnet JTAG IR->code run
μTVM Runtime C Code Generator μDevice API infer.c infer.o vendor gcc remap infer OpenOCD ld linker telnet JTAG IR->code run
μTVM Runtime C Code Generator μDevice API infer.c infer.o vendor gcc remap infer OpenOCD ld linker telnet JTAG custom loader IR->code run
μTVM Runtime C Code Generator μDevice API infer.c infer.o vendor gcc remap infer OpenOCD ld linker telnet JTAG custom loader IR->code send program run
Get in touch! Pratyush Patel — patelp1@cs.uw.edu