PaGoRi:A Scalable Parallel Golomb-Rice Decoder

Deep Neural Networks (DNNs) have created opportunities to address real-world issues and expand the application of Artificial Intelligence (AI). Despite significant accuracy enhancements, DNNs pose a challenge when deployed on resource-limited edge devices commonly used in Internet of Things (IoT) applications. Inference execution of the DNNs requires accessing millions of parameters responsible for most energy consumption. Compression of weights is one possible solution, but most of the existing hardware decompression units could be more efficient in terms of power, area, and energy. This paper presents a scalable version of a hardware-efficient Parallel Golomb-Rice decoder (PaGoRi). The decoder has been integrated with an industry-strength Neural Network (NN) accelerator and evaluated with three TinyML benchmarks. The PaGoRi decoder achieves optimal trade-offs between power consumption and throughput, supporting decoding capacities of four and eight weights, consuming 0.43 mW and 0.79 mW of power, respectively, while achieving a throughput of 888 MBps and 1.3 GBps, respectively.