An 86 mW 98GOPS ANN-Searching Processor for Full-HD 30 fps Video Object Recognition With Zeroless Locality-Sensitive Hashing

Abstract

Approximate nearest neighbor (ANN) searching is an essential task in object recognition. The ANN-searching stage, however, is the main bottleneck in the object recognition process due to increasing database size and massive dimensions of key-point descriptors. In this paper, a high throughput ANN-searching processor is proposed for high-resolution (full-HD) and real-time (30 fps) video object recognition. The proposed ANN-searching processor adopts an interframe cache architecture as a hardware-oriented approach and a zeroless locality-sensitive-hashing (zeroless-LSH) algorithm as a software-oriented approach to reduce the external memory bandwidth required in nearest neighbor searching. A four-way set associative on-chip cache has a dedicated architecture to exploit data correlation at the frame-level. Zeroless-LSH minimizes data transactions from external memory at the vector-level. The proposed ANN-searching processor is fabricated as part of an object recognition SoC using a 0.13 mu m 6 metal CMOS technology. It achieves 62 720 vectors/s throughput and 1140 GOPS/W power efficiency, which are 1.45 and 1.37 times higher than the state-of-the-art, respectively, enabling real-time object recognition for full-HD 30 fps video streams.