|author||Anton Sorokin <firstname.lastname@example.org>||Wed Sep 08 18:10:14 2021 -0700|
|committer||GitHub <email@example.com>||Wed Sep 08 18:10:14 2021 -0700|
VTA Chisel Wide memory interface. (#32) * VTA Chisel Wide memory interface. * Added SyncQueue with tests - Implementation uses sync memory to implement larger queues. * AXI 64/128/256/512 data bits support by AXIParams->dataBits A wide implementation of load/store is used when AXI interface data width is larger than number of bits in a tesor. Instructions are stored as 64bit tensors to allow 64bit address alignment * TensorLoad is modified to replace all VME load operations. Multiple simultaneous requests can be generated. Load is pipelined and separated from request generation. * TensorStore -> TensorStoreNarrowVME TensorStoreWideVME. The narrow one is the original one * TensorLoad -> TensorLoadSimple (original) TensorLoadWideVME TensorStoreNarrowVME * LoadUop -> LoadUopSimple is the original one. The new one is based on TensorLoad * Fetch -> FetchVME64 FetchWideVME. Reuse communication part from TensorLoad. * DPI intreface changed to transfer more than 64bit. svOpenArrayHandle is used. tsim library compilation now requires verilator * Compute is changed to use TensorLoad style of load uop. * VME changed to generate/queue/respond to multiple simultaneous load requests * code formatting fix * Update to Chisel 3.4.3 PR Port to the latest stable Chisel release (#33) * Fix Makefile to use Chisel -o instead of top name and .sv instead of .v * fix reset to reset.asBool * fix SyncQueue to deprecated module.io * fix toBools to asBools * include Verialted.cpp verilated_dpi.cpp directly in module.cc to provide verilator array acces fuctionality and avoid compilation warnings * fix module io warnings * comments * Jenkinsfile ci pipeline fix * Jenkinsfile ci pipeline fix. only for lint,cpu,i386 * Reenable tsim tests * style fix * comments cleanup * AXI constants commented. Moved write id to VME * comments cleanup * comments cleanup
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: