High-performance, very low power content-based search engine

Content-based search, such as audio/video fingerprinting, identifies a piece of query content by matching its perceptual features against those from a database of reference content. Such matching is challenging in both scalability and robustness, even with state-of-art methods like Locality Sensitive Hashing (LSH). Previously, Vote Count, a hardware-assisted algorithm, was proposed to provide such scalability and robustness. We have analyzed this algorithm and found that it would however consume very high power, to the point of even making cooling impractical. In this paper, we propose an alternative hardware-assisted search algorithm that is estimated to use very low power while providing scalability and robustness. It is estimated to be able to provide ≥0.95 recall on a 1-Trillion feature vector (~23M hours of video at 12fps signature rate) database within 700μs at <; 150W, when the hashing bit error rate (BER) would have been 20% even with 1-bit quantization. This amounts to over 1000× power and energy savings compared to highly competitive configurations of LSH, while at lower expected system cost and a saving of millions of dollars per year in electricity cost alone.