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Jean-Pierre David

Alumni

Publications

BARVINN: Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPU
Mohammadhossein Askarihemmat
Sean Wagner
Olexa Bilaniuk
Yassine Hariri
Yvon Savaria
Jean-Pierre David
We present a DNN accelerator that allows inference at arbitrary precision with dedicated processing elements that are configurable at the bi… (voir plus)t level. Our DNN accelerator has 8 Processing Elements controlled by a RISC-V controller with a combined 8.2 TMACs of computational power when implemented with the recent Alveo U250 FPGA platform. We develop a code generator tool that ingests CNN models in ONNX format and generates an executable command stream for the RISC-V controller. We demonstrate the scalable throughput of our accelerator by running different DNN kernels and models when different quantization levels are selected. Compared to other low precision accelerators, our accelerator provides run time programmability without hardware reconfiguration and can accelerate DNNs with multiple quantization levels, regardless of the target FPGA size. BARVINN is an open source project and it is available at https://github.com/hossein1387/BARVINN.
2023-01-30
Proceedings of the 28th Asia and South Pacific Design Automation Conference (publié)
doi.org
arxiv.org