Accelerating DNNs Using Weight Clustering on RISC-V Custom Functional Units

Weight clustering is typically used to compress a Deep Neural Network (DNN) by reducing the number of unique weight values, which can be encoded using a few bits. However, using weight clustering for acceleration remains an unexplored area. In this work, we propose a design using Custom Functional Units (CFUs) to accelerate DNNs with weight clustering on a RISC-V-based
$\mathbf{SoC}$
. We evaluate our accelerator on resource-constrained ML use cases and are able to report considerable speedups of up to 8 times with minimal overhead in the utilization of FPGA resources.