From 7094b3564a9a238f637b872519dfaf5e64eb877f Mon Sep 17 00:00:00 2001 From: Jason Ho Date: Wed, 13 Aug 2025 19:36:45 -0500 Subject: [PATCH 1/6] Create LICENSE --- LICENSE | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..c756d16 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Jason Ho + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. From 3cfff216fcf9991098ca085ffb15ecc46f689f1c Mon Sep 17 00:00:00 2001 From: Jason Ho Date: Thu, 14 Aug 2025 14:56:57 -0500 Subject: [PATCH 2/6] Update README.md --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 6c6867e..fd7b3d1 100644 --- a/README.md +++ b/README.md @@ -3,12 +3,13 @@ **Affiliation:** The University of Texas at Austin, System-Level Architecture and Modeling Group (SLAM Lab) \ **Conference:** Machine Learning on Computer-Aided Design (MLCAD) 2025 \ **Corresponding Contact**: jason_ho@utexas.edu +**Code Ocean DOI**: [[https://doi.org/10.24433/CO.1005356.v1]](https://doi.org/10.24433/CO.1005356.v1) ### Abstract Neuromorphic systems using in-memory or event-driven computing are motivated by the need for more energy-efficient processing of artificial intelligence workloads. Emerging neuromorphic architectures aim to combine traditional digital designs with the computational efficiency of analog computing and novel device technologies. A crucial problem in the rapid exploration and co-design of such architectures is the lack of tools for fast and accurate modeling and simulation. Typical mixed-signal design tools integrate a digital simulator with an analog solver like SPICE, which is prohibitively slow for large systems. By contrast, behavioral modeling of analog components is faster, but existing approaches are fixed to specific architectures with limited energy and performance modeling. In this paper, we propose LASANA, a novel approach that leverages machine learning to derive data-driven surrogate models of analog sub-blocks in a digital backend architecture. LASANA uses SPICE-level simulations of a circuit to train ML models that predict circuit energy, performance, and behavior at analog/digital interfaces. Such models can provide energy and performance annotation on top of existing behavioral models or function as replacements to analog simulation. We apply LASANA to an analog crossbar array and a spiking neuron circuit. Running MNIST and spiking MNIST, LASANA surrogates demonstrate up to three orders of magnitude speedup over SPICE, with energy, latency, and behavioral error less than 7%, 8%, and 2%, respectively. -[[arXiv]](https://arxiv.org/abs/2507.10748) [[Code Ocean]](optional-link) +[[arXiv]](https://arxiv.org/abs/2507.10748) [[Code Ocean]](https://doi.org/10.24433/CO.1005356.v1) --- ### Overview @@ -193,4 +194,4 @@ J. Ho, J. A. Boyle, L. Liu, and A. Gerstlauer, “LASANA: Large‑Scale Surrog year = {2025}, note = {Accepted to MLCAD 2025}, doi = {10.48550/arXiv.2507.10748} -} \ No newline at end of file +} From acee27f72881ceecca03d66c8c2e904335b3999f Mon Sep 17 00:00:00 2001 From: Jason Ho Date: Thu, 14 Aug 2025 14:57:15 -0500 Subject: [PATCH 3/6] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index fd7b3d1..77aeee5 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ **Authors:** Jason Ho, James. A. Boyle, Linshen Liu, and Andreas Gerstlauer \ **Affiliation:** The University of Texas at Austin, System-Level Architecture and Modeling Group (SLAM Lab) \ **Conference:** Machine Learning on Computer-Aided Design (MLCAD) 2025 \ -**Corresponding Contact**: jason_ho@utexas.edu +**Corresponding Contact**: jason_ho@utexas.edu \ **Code Ocean DOI**: [[https://doi.org/10.24433/CO.1005356.v1]](https://doi.org/10.24433/CO.1005356.v1) From e41a88d85db86d2ca96b8dc31e16e5aadc3a4cce Mon Sep 17 00:00:00 2001 From: Jason Ho Date: Thu, 14 Aug 2025 14:57:45 -0500 Subject: [PATCH 4/6] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 77aeee5..d1e9851 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ **Affiliation:** The University of Texas at Austin, System-Level Architecture and Modeling Group (SLAM Lab) \ **Conference:** Machine Learning on Computer-Aided Design (MLCAD) 2025 \ **Corresponding Contact**: jason_ho@utexas.edu \ -**Code Ocean DOI**: [[https://doi.org/10.24433/CO.1005356.v1]](https://doi.org/10.24433/CO.1005356.v1) +**Code Ocean DOI**: [https://doi.org/10.24433/CO.1005356.v1](https://doi.org/10.24433/CO.1005356.v1) ### Abstract From eefc36ab759f76fb0ec441575806eebc06345112 Mon Sep 17 00:00:00 2001 From: Jason Ho Date: Thu, 4 Dec 2025 13:35:08 -0600 Subject: [PATCH 5/6] Update README.md after ieeexplore --- README.md | 42 ++++++++++++++++++++++++------------------ 1 file changed, 24 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index d1e9851..d177a38 100644 --- a/README.md +++ b/README.md @@ -4,14 +4,37 @@ **Conference:** Machine Learning on Computer-Aided Design (MLCAD) 2025 \ **Corresponding Contact**: jason_ho@utexas.edu \ **Code Ocean DOI**: [https://doi.org/10.24433/CO.1005356.v1](https://doi.org/10.24433/CO.1005356.v1) +**Paper DOI**: [https://doi.org/10.1109/MLCAD65511.2025.11189133](https://doi.org/10.1109/MLCAD65511.2025.11189133) ### Abstract Neuromorphic systems using in-memory or event-driven computing are motivated by the need for more energy-efficient processing of artificial intelligence workloads. Emerging neuromorphic architectures aim to combine traditional digital designs with the computational efficiency of analog computing and novel device technologies. A crucial problem in the rapid exploration and co-design of such architectures is the lack of tools for fast and accurate modeling and simulation. Typical mixed-signal design tools integrate a digital simulator with an analog solver like SPICE, which is prohibitively slow for large systems. By contrast, behavioral modeling of analog components is faster, but existing approaches are fixed to specific architectures with limited energy and performance modeling. In this paper, we propose LASANA, a novel approach that leverages machine learning to derive data-driven surrogate models of analog sub-blocks in a digital backend architecture. LASANA uses SPICE-level simulations of a circuit to train ML models that predict circuit energy, performance, and behavior at analog/digital interfaces. Such models can provide energy and performance annotation on top of existing behavioral models or function as replacements to analog simulation. We apply LASANA to an analog crossbar array and a spiking neuron circuit. Running MNIST and spiking MNIST, LASANA surrogates demonstrate up to three orders of magnitude speedup over SPICE, with energy, latency, and behavioral error less than 7%, 8%, and 2%, respectively. -[[arXiv]](https://arxiv.org/abs/2507.10748) [[Code Ocean]](https://doi.org/10.24433/CO.1005356.v1) +[[arXiv]](https://arxiv.org/abs/2507.10748) [[Code Ocean]](https://doi.org/10.24433/CO.1005356.v1) [[IEEE Paper]](https://doi.org/10.1109/MLCAD65511.2025.11189133) --- + +## Citation +If you use this work, please cite our paper: + +J. Ho, J. A. Boyle, L. Liu and A. Gerstlauer, "LASANA: Large-Scale Surrogate Modeling for Analog Neuromorphic Architecture Exploration," 2025 ACM/IEEE 7th Symposium on Machine Learning for CAD (MLCAD), Santa Cruz, CA, USA, 2025, pp. 1-8, doi: 10.1109/MLCAD65511.2025.11189133. + +**BibTeX** +``` +@INPROCEEDINGS{Ho2025LASANA, + author={Ho, Jason and Boyle, James A. and Liu, Linshen and Gerstlauer, Andreas}, + booktitle={2025 ACM/IEEE 7th Symposium on Machine Learning for CAD (MLCAD)}, + title={LASANA: Large-Scale Surrogate Modeling for Analog Neuromorphic Architecture Exploration}, + year={2025}, + volume={}, + number={}, + pages={1-8}, + keywords={Solid modeling;Neuromorphics;Computational modeling;Neurons;Machine learning;Predictive models;SPICE;Energy efficiency;Design tools;Computational efficiency;neuromorphic architectures;analog computing;mixed-signal simulation;machine learning;surrogate modeling}, + doi={10.1109/MLCAD65511.2025.11189133}} +``` + +--- + ### Overview This repository contains the code and scripts used for our paper: LASANA: Large-Scale Surrogate Modeling for Analog Neuromorphic Architecture Exploration, accepted at MLCAD 2025. The code is structured for a Code Ocean capsule (link above), an immutable, reproducible container that contains code, datasets, and outputs from a run on their systems. We detail some of the limitations / guardrails put in place to successfully emulate our environment and results using Code Ocean below. @@ -178,20 +201,3 @@ Additionally, near the end of the script, we can uncomment line 160, and line 16 ## Dependencies Our runs, the Code Ocean runs, etc. have all been run in Python 3.8.*. We have also provided the essential dependencies in `requirements.txt`, if one wants to recreate the same results locally. - - -## Citation -If you use this work, please cite our paper: - -J. Ho, J. A. Boyle, L. Liu, and A. Gerstlauer, “LASANA: Large‑Scale Surrogate Modeling for Analog Neuromorphic Architecture Exploration,” arXiv preprint arXiv:2507.10748, 2025, accepted at MLCAD 2025. - -**BibTeX** -``` -@article{Ho2025LASANA, - author = {Ho, Jason and Boyle, James A. and Liu, Linshen and Gerstlauer, Andreas}, - title = {LASANA: Large-Scale Surrogate Modeling for Analog Neuromorphic Architecture Exploration}, - journal = {arXiv preprint arXiv:2507.10748}, - year = {2025}, - note = {Accepted to MLCAD 2025}, - doi = {10.48550/arXiv.2507.10748} -} From 6020c18274f6f82ad965d86507276e7fb5682cac Mon Sep 17 00:00:00 2001 From: Jason Ho Date: Thu, 4 Dec 2025 13:36:18 -0600 Subject: [PATCH 6/6] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index d177a38..5f853d0 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ **Affiliation:** The University of Texas at Austin, System-Level Architecture and Modeling Group (SLAM Lab) \ **Conference:** Machine Learning on Computer-Aided Design (MLCAD) 2025 \ **Corresponding Contact**: jason_ho@utexas.edu \ -**Code Ocean DOI**: [https://doi.org/10.24433/CO.1005356.v1](https://doi.org/10.24433/CO.1005356.v1) +**Code Ocean DOI**: [https://doi.org/10.24433/CO.1005356.v1](https://doi.org/10.24433/CO.1005356.v1) \ **Paper DOI**: [https://doi.org/10.1109/MLCAD65511.2025.11189133](https://doi.org/10.1109/MLCAD65511.2025.11189133) @@ -20,7 +20,7 @@ If you use this work, please cite our paper: J. Ho, J. A. Boyle, L. Liu and A. Gerstlauer, "LASANA: Large-Scale Surrogate Modeling for Analog Neuromorphic Architecture Exploration," 2025 ACM/IEEE 7th Symposium on Machine Learning for CAD (MLCAD), Santa Cruz, CA, USA, 2025, pp. 1-8, doi: 10.1109/MLCAD65511.2025.11189133. **BibTeX** -``` +```bibtex @INPROCEEDINGS{Ho2025LASANA, author={Ho, Jason and Boyle, James A. and Liu, Linshen and Gerstlauer, Andreas}, booktitle={2025 ACM/IEEE 7th Symposium on Machine Learning for CAD (MLCAD)},