In-Memory Computing with Non-volatile Memristor CAM Circuits

Graves, Catherine E.; Li, Can; Pedretti, Giacomo; Strachan, John Paul

doi:10.1007/978-3-030-90582-8_6

Cited by 4 publications

(1 citation statement)

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“…In-memory search is a promising hardware paradigm where search and computing tasks are performed on content-addressable memories (CAMs), especially emerging nonvolatile memorybased CAMs, such as memristor, [11][12][13][14][15] phase change memory (PCM) [16][17][18] and ferroelectric transistor. [19][20][21][22] The nonvolatile CAMs have shown great promise in machine learning, [23][24][25] pattern matching, [26][27][28] and neural networks [29][30][31] due to their massive parallelism. However, due to the no-ideal factor such as device variation, the accuracy loss of image search is inevitable.…”

Section: Introductionmentioning

confidence: 99%

In‐Memory Search for Highly Efficient Image Retrieval

Ling

Zhou

et al. 2023

Advanced Intelligent Systems

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Finding similar images in real time plays a key role in information retrieval and serves as an indispensable function of the search engine. However, image retrieval involves massive distance computation. With the increase in image data volume and dimension, distance computation is suffering from huge power consumption and high computational complexity. Despite the remarkable advantages in energy efficiency shown by nonvolatile content addressable memory (nvCAM)‐based in‐memory search, achieving software‐comparable search accuracy remains a critical challenge under the impact of device variations and other nonideal factors. Here, a heterogeneous image retrieval system combining highly parallel in‐memory search with a high‐precision digital system is reported. Hamming distance (HD) can be calculated in situ with a few memory read operations on the memristor‐based CAM, and several similar images are fetched for further high‐precision rerank in the digital system. This heterogeneous computing system shows high energy efficiency (50×) compared to the CPU and higher search accuracy than the fully in‐memory computing method, thus alleviating the efficiency bottleneck of CPU‐based image retrieval.

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