Bayesian Optimization of Crossbar-Based Compute-In-Memory System Design for Efficient DNN Inference
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918491086389248 |
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| author | Saha, Arnob Manna, Bibhas Kotikalapudi, Nikhil Mia, Md Zesun Ahmed Kumar, Rahul Swaminathan, Madhavan Sengupta, Abhronil |
| author_facet | Saha, Arnob Manna, Bibhas Kotikalapudi, Nikhil Mia, Md Zesun Ahmed Kumar, Rahul Swaminathan, Madhavan Sengupta, Abhronil |
| contents | 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 $91.72 \%$ and $57.2 \%$ accuracy, respectively, comparable to baseline designs, while improving chip area ($65.52 \%$ and $50.7 \%$), read latency ($9.52 \%$ and $13.27 \%$), read dynamic energy ($31.23 \%$ and $52.07 \%$) and increasing memory utilization ($13.41 \%$ and $2.67 \%$). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08461 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bayesian Optimization of Crossbar-Based Compute-In-Memory System Design for Efficient DNN Inference Saha, Arnob Manna, Bibhas Kotikalapudi, Nikhil Mia, Md Zesun Ahmed Kumar, Rahul Swaminathan, Madhavan Sengupta, Abhronil Emerging Technologies 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 $91.72 \%$ and $57.2 \%$ accuracy, respectively, comparable to baseline designs, while improving chip area ($65.52 \%$ and $50.7 \%$), read latency ($9.52 \%$ and $13.27 \%$), read dynamic energy ($31.23 \%$ and $52.07 \%$) and increasing memory utilization ($13.41 \%$ and $2.67 \%$). |
| title | Bayesian Optimization of Crossbar-Based Compute-In-Memory System Design for Efficient DNN Inference |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2605.08461 |