Optimizing Data Compression: Enhanced Golomb-Rice Encoding with Parallel Decoding Strategies for TinyML Models

Deep Neural Networks (DNNs) offer possibilities for tackling practical challenges and broadening the scope of Artificial Intelligence (AI) applications. The demanding memory requirements of present-day neural networks can be attributed to the rising intricacy of network architectures. These designs encompass multiple layers with an extensive number of parameters, leading to heightened demands on memory storage. The energy consumption during the inference execution of DNNs is predominantly attributed to the access and processing of these parameters. To tackle the significant size of models integrated into Internet of Things (IoT) devices, a promising strategy involves diminishing the bit width of weights. This paper introduces an improved version of Golomb-Rice (GR) encoder and an optimized Parallel Golomb-Rice decoder that can support sparse and non-sparse DNNs. To evaluate the encoder's and decoder's efficiency, we conducted two sets of experiments using three TinyML benchmarks, one without pruning and the other incorporating pruning. The results highlight that the encoder demonstrates a Compression-Ratio (CR) superior to that of Huffman encoding, and the decoder exhibits an energy efficiency of up to 2.6 TBps/W and 2.7 TBps/W for four- and eight-weight decoding, respectively.