commit	6bb74c0efd64c30f43353364a9bfb6923585d628	[log] [tgz]
author	Liangfu Chen <liangfu.chen@harman.com>	Sat Jan 18 07:23:49 2020 +0800
committer	Thierry Moreau <tmoreau@octoml.ai>	Fri Jan 17 15:23:49 2020 -0800
tree	bb2eb87573dc9e21ce810d1a69833520ed6bb82e
parent	57f9d7f44bbf3ad65c6123d8e902eec0398a683b [diff]

[VTA][TSIM] Enable TSIM CI Testing (#4407) * Update task_python_vta.sh * install sbt=1.1.1 with apt-get * update verilator_opt * install verilator with major version 4.0 * disable multi-threading for now * bug fix for correcting uop fetch address in LoadUop module * bug fix for correcting uop fetch address in LoadUop module * adjustment to read from dram_offset * enable USE_THREADS with verilator 4.x * DEBUG: try avoid core dump with verilator 4.x * bug fix in LoadUop module * log mega cycles in tsim * download cat.png to avoid fetching in each run * bug fix in LoadUop module * solve dram_even/sram_even issue * bug fix * introduce scalalint in ci * speedup tsim in ci * bug fix * lint scala code before building * disable multi-threading * split fsim/tsim script * update Jenkins settings * duplicate task_python_vta_fsim.sh as task_python_vta.sh for now Co-authored-by: Thierry Moreau <tmoreau@octoml.ai>

tree: bb2eb87573dc9e21ce810d1a69833520ed6bb82e

README.md

VTA: Open, Modular, Deep Learning Accelerator Stack

VTA (versatile tensor accelerator) is an open-source deep learning accelerator complemented with an end-to-end TVM-based compiler stack.

The key features of VTA include:

Generic, modular, open-source hardware
- Streamlined workflow to deploy to FPGAs.
- Simulator support to prototype compilation passes on regular workstations.
Driver and JIT runtime for both simulator and FPGA hardware back-end.
End-to-end TVM stack integration
- Direct optimization and deployment of models from deep learning frameworks via TVM.
- Customized and extensible TVM compiler back-end.
- Flexible RPC support to ease deployment, and program FPGAs with the convenience of Python.

Learn more about VTA here.