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Compute-in-Memory Attention: A Time-Domain Analog Softmax Circuit with RC-Tunable Temperature

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Softmax is a key operation in Transformer attention, but its exponentiation and normalization add significant overhead in compute-in-memory (CIM) accelerators, especially when analog attention scores must first be converted to the digital domain. This work presents a tunable-temperature analog softmax circuit in GlobalFoundries 22-nm fully depleted silicon-on-insulator (FDSOI) technology that operates directly on CIM-generated score voltages without intermediate analog-to-digital conversion. Each input score is converted into a time-domain event using a shared falling ramp. The corresponding c

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