  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
https://github.com/hossein1387/BARVINN.
