UC San Diego

The rapid proliferation of data has expedited the development of cutting-edge machine learning (ML) methods, including deep neural networks, to find underlying insights from data. However, the large amount of data movement has driven up energy requirements, posing a new threat of surpassing global energy production. In this dissertation, we explore a novel computing paradigm, hyperdimensional computing (HDC), which mimics attributes of the human brain's neuronal circuits with lightweight arithmetic on low-precision high-dimensional vectors and significantly enhances speed and energy efficiency. We present OpenHD, a GPU-based software infrastructure for HDC, which automatically generates optimized GPU code of HDC applications with the Just-in-Time compilation to ease the development and optimization efforts for the deployment. The proposed framework achieves 4.5x and 146x speedup on average for GPU-based HDC classification and clustering, respectively.

Using OpenHD, we first expand the variety of data structures that can be supported in HDC applications. We design an HDC-based ML solution that supports graph data, and implement it on GPU. Our solution, RelHD, enables graph-based ML by aggregating the relationship between data and features of data into a single high-dimensional vector. We further accelerate RelHD using processing in-memory (PIM) based on FeFET. The PIM accelerator offers 10x speedup and 986x energy efficiency improvement over the state-of-the-art crossbar memory-based graph neural network accelerator.

While most existing HDC-based applications have used small-scale datasets, we show that HDC can scale to tackle the large-scale problem. We present an HDC-based approach called HyperOMS for large-scale open modification spectral library searching (OMS) in mass spectrometry-based proteomics analysis. We develop a novel HDC-based OMS algorithm and accelerate it on GPU using the OpenHD framework. To run HyperOMS efficiently, we devise a DRAM-based PIM accelerator with optimization strategies to maximize parallelism. Evaluation results show that the accelerator yields up to 100x speedup and has 1,337x higher energy efficiency over the state-of-the-art OMS tool running on GPU while offering comparable search quality to competing solutions.