A 92m W 76.8GOPS vector matching processor with parallel Huffman decoder and query re-ordering buffer for real-time object recognition

A vector matching processor with memory bandwidth optimizations is proposed to achieve real-time matching of 128 dimensional SIFT features extracted from VGA video. The main bottleneck of feature-vector matching is the off-chip database access. We employ the locality sensitive hashing (LSH) algorithm which reduces the number of database comparisons required to match each query. In addition, database compression using Huffman coding increases the effective external bandwidth. Dedicated parallel Huffman decoder hardware ensures fast decompression of the database. A flexible query re-ordering buffer exploits overlapping accesses between queries by enabling out-of-order query processing to minimize redundant off-chip access. As a result, the 76.8 GOPS feature matching processor implemented in a 0.13um CMOS process achieves 43200 queries/second on a 100 object database while consuming peak power of 92mW.