NNgine: Ultra-Efficient Nearest Neighbor Accelerator Based on In-Memory Computing

Abstract

The nearest neighbor (NN) algorithm has been used in a broad range of applications including pattern recognition, classification, computer vision, databases, etc. The NN algorithm tests data points to find the nearest data to a query data point. With the Internet of Things the amount of data to search through grows exponentially, so we need to have more efficient NN design. Running NN on multicore processors or on general purpose GPUs has significant energy and performance overhead due to small available cache sizes resulting in moving a lot of data via limited bandwidth busses from memory. In this paper, we propose a nearest neighbor accelerator, called NNgine, consisting of ternary content addressable memory (TCAM) blocks which enable near-data computing. The proposed NNgine overcomes energy and performance bottleneck of traditional computing systems by utilizing multiple non-volatile TCAMs which search for nearest neighbor data in parallel. We evaluate the efficiency of our NNgine design by comparing to existing processor-based approaches. Our results show that NNgine can achieve 5590x higher energy efficiency and 510x speed up compared to the state-of-the-art techniques with a negligible accuracy loss of 0.5%.