Bayesian Optimization of Crossbar-Based Compute-In-Memory System Design for Efficient DNN Inference

Leveraging the high density and energy efficiency of Compute-In-Memory (CIM) crossbar-based Deep Neural Network (DNN) accelerators requires optimal Design Space Exploration (DSE), which becomes increasingly challenging as complex models for advanced AI workloads expand the highly non-convex design space. Moreover, heterogeneous layer workloads (e.g., memory- vs. compute-bound) and learning representations make layer-wise NN parameter allocation beneficial for efficiency but severely exacerbate the design space complexity by expanding the number of parameters to be tuned for simultaneous multi-objective optimization. Among existing DSE approaches, multi-objective Bayesian Optimization (BO) is promising, as it explores high-quality design solutions while querying costly CIM simulators selectively. In this work, we propose a multi-objective BO framework that holistically co-optimizes hardware and algorithm parameters of a CIM crossbar-based hardware accelerator for various DNN inference tasks. Depending on NN model depth, our framework handles high-dimensional design spaces (with $26$ and $50$ dimensions) and extremely large search complexities on the order of $O({10}^{12})$ and $O({10}^{27})$ for VGG8/CIFAR-10 and VGG16/Tiny-ImageNet-200. Our method attains $91.72\mathrm{\%}$ and $57.2\mathrm{\%}$ accuracy, respectively, comparable to baseline designs, while improving chip area ($65.52\mathrm{\%}$ and $50.7\mathrm{\%}$), read latency ($9.52\mathrm{\%}$ and $13.27\mathrm{\%}$), read dynamic energy ($31.23\mathrm{\%}$ and $52.07\mathrm{\%}$) and increasing memory utilization ($13.41\mathrm{\%}$ and $2.67\mathrm{\%}$).