Dynamic Neural Fields Accelerator Design for a Millimeter-Scale Tracking System

This brief introduces a compact-size hardware accelerator for dynamic neural fields (DNF) used in object tracking. To address the substantial computational workload and memory occupancy associated with conventional DNFs, three key approaches are implemented: kernel size reduction and abstraction, the replacement of sigmoidal functions with comparison operations, and the approximation of rectangular-shaped objects. The design is realized in a 28-nm CMOS process, resulting in a layout with an area of 0.53 mm2. Simulation results demonstrate that the accelerator processes
$256 \times 256$
dynamic vision sensor (DVS) frames at 211 frames per second (fps), with a power consumption of 1.68 mW under such conditions.