A 86mW 98GOPS ANN-searching processor for Full-HD 30fps video object recognition with zeroless locality-sensitive hashing

摘要

Increasing database size and massive dimensions of keypoint descriptors caused nearest neighbor searching as a main bottleneck in object recognition systems. Therefore a high throughput approximate nearest neighbor (ANN) searching processor is proposed for real-time object recognition. To reduce the external bandwidth required in nearest neighbor searching, this chip utilizes an on-chip cache for transaction reduction and the zeroless locality-sensitive hashing (zeroless-LSH) operation for required data suppression. As a result, the proposed ANN-searching processor achieves 62,720 vectors/sec throughput and 1,140GOPS/W power efficiency, which are 1.45x and 1.37x higher than the state-of-the-art respectively, enabling real-time object recognition for Full-HD 30fps video streams.