Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPU (Ecole Polytechnique Montreal, IBM, Mila, CMC)

A new technical paper titled “BARVINN: Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPU” was written by researchers at Ecole Polytechnique Montreal, IBM, Mila and CMC Microsystems. It was accepted for publication in the 2023, 28th Asia and South Pacific Design Automation Conference (ASP-DAC 2023) in Japan.

Abstract:
“We present a DNN accelerator that allows inference at arbitrary precision with dedicated processing elements that are configurable at the bit 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 this https URL.”

Find the technical paper here. Published December 2022.

Askarihemmat, Mohammadhossein, et al. “BARVINN: Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPU.” arXiv preprint arXiv:2301.00290 (2022).

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