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Device and Circuit Architectures for In-Memory Computing
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DOI：10.1002/aisy.202000040.png)
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Abstract
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With the rise in artificial intelligence (AI), computing systems are facing new challenges related to the large amount of data and the increasing burden of communication between the memory and the processing unit. In-memory computing (IMC) appears as a promising approach to suppress the memory bottleneck and enable higher parallelism of data processing, thanks to the memory array architecture. As a result, IMC shows a better throughput and lower energy consumption with respect to the conventional digital approach, not only for typical AI tasks, but also for general-purpose problems such as constraint satisfaction problems (CSPs) and linear algebra. Herein, an overview of IMC is provided in terms of memory devices and circuit architectures. First, the memory device technologies adopted for IMC are summarized, focusing on both charge-based memories and emerging devices relying on electrically induced material modification at the chemical or physical level. Then, the computational memory programming and the corresponding device nonidealities are described with reference to offline and online training of IMC circuits. Finally, array architectures for computing are reviewed, including typical architectures for neural network accelerators, content addressable memory (CAM), and novel circuit topologies for general-purpose computing with low complexity.
Keywords:
artificial intelligence
in-memory computing
machine learning
memories
neural networks
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