Energy Efficient Dual Designs of FeFET-Based Analog In-Memory Computing with Inherent Shift-Add Capability
Zeyu Yang, Qingrong Huang, Yu Qian, Kai Ni, Thomas K\"ampfe and, Xunzhao Yin

TL;DR
This paper introduces energy-efficient FeFET-based analog in-memory computing designs with inherent shift-add capabilities, enhancing multiply-accumulate operations for deep neural networks.
Contribution
It proposes novel dual FeFET-based IMC architectures that integrate shift-add functionality within the memory array, improving energy efficiency and supporting high-precision weights.
Findings
Energy efficiency improved by 1.56x/1.37x over state-of-the-art designs.
Supports both 2's complement and non-2's complement MAC operations.
Enables high-precision analog IMC with integrated shift-add capability.
Abstract
In-memory computing (IMC) architecture emerges as a promising paradigm, improving the energy efficiency of multiply-and-accumulate (MAC) operations within DNNs by integrating the parallel computations within the memory arrays. Various high-precision analog IMC array designs have been developed based on both SRAM and emerging non-volatile memories. These designs perform MAC operations of partial input and weight, with the corresponding partial products then fed into shift-add circuitry to produce the final MAC results. However, existing works often present intricate shift-add process for weight. The traditional digital shift-add process is limited in throughput due to time-multiplexing of ADCs, and advancing the shift-add process to the analog domain necessitates customized circuit implementations, resulting in compromises in energy and area efficiency. Furthermore, the joint…
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Taxonomy
TopicsFerroelectric and Negative Capacitance Devices · Advanced Memory and Neural Computing · Semiconductor materials and devices
