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 Kämpfe, Xunzhao Yin
摘要
In-memory computing (IMC) architecture emerges as a promising paradigm, improving the energy efficiency of multiply-and-accumulate (MAC) operations within deep neural networks (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 (NVMs). 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 optimization of the partial MAC operations and the weight shift-add process is rarely explored. In this paper, we propose novel, energy efficient dual designs of ferroelectric FET (FeFET) based high precision analog IMC featuring inherent shift-add capability. We introduce a FeFET based IMC paradigm that performs partial MAC in each column, and inherently integrates the shift-add process for 4-bit weights by leveraging FeFET's analog storage characteristics. This paradigm supports both 2's complement mode (2CM) and non-2's complement mode (N2CM) MAC, thereby offering flexible support for 4-/8-bit weight data in 2's complement format. Building upon this paradigm, we propose novel FeFET based dual designs, CurFe for the current mode and ChgFe for the charge mode, to accommodate the high precision analog domain IMC architecture. Evaluation results at circuit and system levels indicate that the circuit/system-level energy efficiency of the proposed FeFET-based analog IMC is 1.56×/1.37× higher when compared to the state-of-the-art analog IMC designs.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper1
相关 Paper
- FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory ComputingChao Li, Zhicheng Xu, Bo Wen, Ruibin Mao 等DAC 2024 · 被引用 6 次
- Compact and Efficient CAM Architecture through Combinatorial Encoding and Self-Terminating Searching for In-Memory-Searching AcceleratorWeikai Xu, Jin Luo, Qianqian Huang, Ru HuangDAC 2024 · 被引用 1 次
- FSPA: An FeFET-based Sparse Matrix-Dense Vector Multiplication AcceleratorXiaoyu Zhang, Zerun Li, Rui Liu, Xiaoming Chen 等DAC 2023 · 被引用 10 次
- A Charge-Sharing based 8T SRAM In-Memory Computing for Edge DNN AccelerationKyeongho Lee, Sungsoo Cheon, Joongho Jo, Woong Choi 等DAC 2021 · 被引用 34 次
- Energy efficient data search design and optimization based on a compact ferroelectric FET content addressable memoryJiahao Cai, Mohsen Imani, Kai Ni, Grace Li Zhang 等DAC 2022 · 被引用 17 次
