Weihong Xu, UCSD, presenting on Wed. 9/21/22 at 1:00 pm & 7:00 pm ET.
Hyperdimensional (HD) computing is a lightweight algorithm
with fast learning capability that can efficiently realize classification and
few-shot learning (FSL) workloads. However, traditional von-Neumann
architecture is highly inefficient for HD algorithms due to the limited memory
bandwidth and capacity. Processing in-memory (PIM) is an emerging computing
paradigm which tries to address these issues by using memories as computing units.
Our talk introduces the efficient PIM architecture for HD as well as
application of HD on few-shot classification. First, we show the PIM-based HD
computing architecture on ReRAM, Tri-HD, to accelerate all phases of the
general HD computing pipeline namely, encoding, training, retraining, and
inference. Tri-HD is enabled by efficient in-memory logic operations and shows
orders of magnitude performance improvements over CPU. However, Tri-HD is not
suitable for area and power-constrainted device since it suffers from high HD
encoding complexity and complex dataflow. To this end, we present an algorithm
and PIM co-design, FSL-HD, to realize energy-efficient FSL. FSL-HD
significantly reduces the encoding complexity by >10x and is equipped with
an optimized dataflow for practical ReRAM. As a result, FSL-HD shows superior
FSL accuracy, flexibility, and hardware efficiency compared to state-of-the-art
ReRAM-based FSL accelerators.
