Clustering Classification on FPGAs for Neuromorphic Feature Extraction.

State-of-the-art machine-learning (ML) apps are often expensive in terms of memory and computation required for high-accuracy feature extraction and object classification using frame-based sensors. By contrast, neuromorphic feature-extraction algorithms offer lower memory and compute complexity than traditional ML algorithms, which can improve the scalability of ML apps. These neuromorphic algorithms operate on neuromorphic, event-based sensor data as opposed to traditional, frame-based camera images. These neuromorphic sensors capture events at microsecond resolution with variable bandwidth. Therefore, neuromorphic algorithms require low-latency architectures to enable real-time processing. Field-programmable gate arrays (FPGAs) are reconfigurable-logic devices that can realize low-latency data paths required for real-time neuromorphic computation.