Self-Calibrating Analog Circuitry for Softmax-Scaled Function With Analog Computing-In-Memory

doi:10.1109/TVLSI.2026.3651307

Self-Calibrating Analog Circuitry for Softmax-Scaled Function With Analog Computing-In-Memory

2026-01-13

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DOI：10.1109/TVLSI.2026.3651307

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Analog computing-in-memory (ACIM) has garnered widespread attention due to its advantage of high energy efficiency. However, it faces large power and hardware costs to handle sophisticated nonlinear functions, such as the softmax, due to costly exponentiation and division. Existing digital-domain approaches often rely on dedicated modules to carry out these operations, leading to a cost expensive area and high-power consumption. To address the issues, we propose a self-calibrating analog circuitry for a softmax-scaled function with ACIM. By exploiting transistor subthreshold properties, the work eliminates expensive digital operations while mapping exponentiation and division to successive analog circuits. A self-calibration module further mitigates partial mismatch-induced deviations by dynamically tuning bias voltages, improving overall fitting accuracy and system robustness. The proposed softmax-enabled ACIM work achieves energy efficiency of 55.06–60.08 TOPS/W and 684.15 GOPS/mm² at 4-bit precision. In comparison with the state-of-the-art ACIMs with softmax implications, our proposed work shows higher energy efficiency and area efficiency.

Keywords:

Analog computing

computing-in-memory (CIM)

hardware implementation

softmax function (SF)

Self-Calibrating Analog Circuitry for Softmax-Scaled Function With Analog Computing-In-Memory

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