Optimization of Emerging Analog Compute Systems with Shem
Analog compute paradigms are gaining attention for their potential to overcome the energy and latency limits of digital systems. Analog computations are modeled as dynamical systems for design and optimization, but this process is challenging: the dynamics are governed by nonlinear differential equations without closed-form solutions, are susceptible to hardware nonidealities such as mismatch and noise, and rely on digitally programmable interfaces that complicate system-level reasoning. We present Shem , a framework that enables gradient-based optimization of system-level objectives such as robustness and accuracy on user-defined analog compute models. We leverage the fact that analog compute models are inherently differentiable, and that nonidealities and digital interfaces can be approximated by differentiable functions, making gradient descent a natural and unified approach for optimization. We evaluate Shem across diverse case studies, including an oscillator-based pattern recognizer, a cellular nonlinear network edge detector, an analog-to-information converter integrated with a neural network, and a transmission-line security primitive. In all cases, Shem improves application performance, including signal-to-noise ratio, classification accuracy, and a security metric. In addition, we introduce an autoparallelization algorithm that reduces total optimization runtime by 49–91%.