Frontiers in Edge AI with RISC-V: Hyperdimensional Computing vs. Quantized Neural Networks

Hyperdimensional Computing (HDC) is an emerging paradigm that stands as a compelling alternative to conventional Deep Learning algorithms. HDC holds four key promises. First, the ability to learn from little data. Second, to be robust against noise in this data. HDC also promises to be resilient against errors in the underlying hardware. This includes the memory on which the model is stored and errors in the computations of the operations, which is attributed to the encoding of information across an expansive dimensional space. Fourth, HDC can be implemented efficiently in hardware due to its lightweight and embarrassingly parallel computations. In this work, those four key promises are evaluated in a holistic way. A fixed-point and a binary HDC implementation are compared against neural network implementations. The models are executed on a RISC-V processor to ensure a fair comparison. While the results confirm the ability to learn from little data and the resiliency against errors, the higher inference accuracy of neural networks favors them in most experiments. Based on these insights, we formulate challenges and opportunities for HDC. Our implementations for QNN, binary and fixed-point HDC are available online: https://github.com/TUM-AIPro/HDC-vs-QNN