{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/analog-foundation-models","title":"Analog Foundation Models","arxiv_id":"2505.09663","date":"2025-05-14","proceeding":null,"authors":["Julian Büchel","Iason Chalas","Giovanni Acampa","An Chen","Omobayode Fagbohungbe","Sidney Tsai","Kaoutar El Maghraoui","Manuel Le Gallo","Abbas Rahimi","Abu Sebastian"],"abstract":"Analog in-memory computing (AIMC) is a promising compute paradigm to improve speed and power efficiency of neural network inference beyond the limits of conventional von Neumann-based architectures. However, AIMC introduces fundamental challenges such as noisy computations and strict constraints on input and output quantization. Because of these constraints and imprecisions, off-the-shelf LLMs are not able to achieve 4-bit-level performance when deployed on AIMC-based hardware. While researchers previously investigated recovering this accuracy gap on small, mostly vision-based models, a generic method applicable to LLMs pre-trained on trillions of tokens does not yet exist. In this work, we introduce a general and scalable method to robustly adapt LLMs for execution on noisy, low-precision analog hardware. Our approach enables state-of-the-art models $\\unicode{x2013}$ including Phi-3-mini-4k-instruct and Llama-3.2-1B-Instruct $\\unicode{x2013}$ to retain performance comparable to 4-bit weight, 8-bit activation baselines, despite the presence of analog noise and quantization constraints. Additionally, we show that as a byproduct of our training methodology, analog foundation models can be quantized for inference on low-precision digital hardware. Finally, we show that our models also benefit from test-time compute scaling, showing better scaling behavior than models trained with 4-bit weight and 8-bit static input quantization. Our work bridges the gap between high-capacity LLMs and efficient analog hardware, offering a path toward energy-efficient foundation models. Code is available at https://github.com/IBM/analog-foundation-models.","url_abs":"https://arxiv.org/abs/2505.09663v2","url_pdf":"https://arxiv.org/pdf/2505.09663v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"analog-foundation-models","repo_url":"https://github.com/ibm/analog-foundation-models","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"4k","task_name":"4k"},{"task_slug":"quantization","task_name":"Quantization"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2505.09663","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.09663"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/IBM/analog-foundation-models","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ibm/analog-foundation-models","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":7},"by_repo_kind":{"official":{"samples":7,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"f15d5270a08bfc3a","entry":"add_PCM_noise","repo":"IBM/analog-foundation-models","repo_kind":"official","path":"apply_noise_model.py","file_url":"https://github.com/IBM/analog-foundation-models/blob/HEAD/apply_noise_model.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f15d5270a08bfc3a"}},{"code_sha256_prefix":"9ee3773063b09df7","entry":"check_and_eval_args","repo":"IBM/analog-foundation-models","repo_kind":"official","path":"src/analog_foundation_models/utils/task_parser.py","file_url":"https://github.com/IBM/analog-foundation-models/blob/HEAD/src/analog_foundation_models/utils/task_parser.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9ee3773063b09df7"}},{"code_sha256_prefix":"bc005d2113d97abf","entry":"distillation_loss","repo":"IBM/analog-foundation-models","repo_kind":"official","path":"src/analog_foundation_models/utils/train_utils.py","file_url":"https://github.com/IBM/analog-foundation-models/blob/HEAD/src/analog_foundation_models/utils/train_utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"bc005d2113d97abf"}},{"code_sha256_prefix":"82c097f83d984d98","entry":"get_split_sizes","repo":"IBM/analog-foundation-models","repo_kind":"official","path":"apply_noise_model.py","file_url":"https://github.com/IBM/analog-foundation-models/blob/HEAD/apply_noise_model.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"82c097f83d984d98"}},{"code_sha256_prefix":"c76456c9c834db32","entry":"maybe_inf2float","repo":"IBM/analog-foundation-models","repo_kind":"official","path":"src/analog_foundation_models/utils/task_parser.py","file_url":"https://github.com/IBM/analog-foundation-models/blob/HEAD/src/analog_foundation_models/utils/task_parser.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c76456c9c834db32"}},{"code_sha256_prefix":"b646d7ce8db58938","entry":"polyval","repo":"IBM/analog-foundation-models","repo_kind":"official","path":"apply_noise_model.py","file_url":"https://github.com/IBM/analog-foundation-models/blob/HEAD/apply_noise_model.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b646d7ce8db58938"}},{"code_sha256_prefix":"a036fa04368b6e4e","entry":"speed_metrics","repo":"IBM/analog-foundation-models","repo_kind":"official","path":"src/analog_foundation_models/utils/train_utils.py","file_url":"https://github.com/IBM/analog-foundation-models/blob/HEAD/src/analog_foundation_models/utils/train_utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a036fa04368b6e4e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}