Acore-CIM: build accurate and reliable mixed-signal CIM cores with RISC-V controlled self-calibration

[2506.15440] Acore-CIM: build accurate and reliable mixed-signal CIM cores with RISC-V controlled self-calibration Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Hardware Architecture arXiv:2506.15440 (cs) [Submitted on 18 Jun 2025] Title: Acore-CIM: build accurate and reliable mixed-signal CIM cores with RISC-V controlled self-calibration Authors: Omar Numan , Gaurav Singh , Kazybek Adam , Jelin Leslin , Aleksi Korsman , Otto Simola , Marko Kosunen , Jussi Ryynänen , Martin Andraud View a PDF of the paper titled Acore-CIM: build accurate and reliable mixed-signal CIM cores with RISC-V controlled self-calibration, by Omar Numan and 8 other authors View PDF HTML (experimental) Abstract: Developing accurate and reliable Compute-In-Memory (CIM) architectures is becoming a key research focus to accelerate Artificial Intelligence (AI) tasks on hardware, particularly Deep Neural Networks (DNNs). In that regard, there has been significant interest in analog and mixed-signal CIM architectures aimed at increasing the efficiency of data storage and computation to handle the massive amount of data needed by DNNs. Specifically, resistive mixed-signal CIM cores are pushed by recent progresses in emerging Non-Volatile Memory (eNVM) solutions. Yet, mixed-signal CIM computing cores still face several integration and reliability challenges that hinder their large-scale adoption into end-to-end AI computing systems. In terms of integration, resistive and eNVM-based CIM cores need to be integrated with a control processor to realize end-to-end AI acceleration. Moreover, SRAM-based CIM architectures are still more efficient and easier to program than their eNVM counterparts. In terms of reliability, analog circuits are more susceptible to variations, leading to computation errors and degraded accuracy. This work addresses these two challenges by proposing a self-calibrated mixed-signal CIM accelerator SoC, fabricated in 22-nm FDSOI technology. The integration is facilitated by (1) the CIM architecture, combining the density and ease of SRAM-based weight storage with multi-bit computation using linear resistors, and (2) an open-source programming and testing strategy for CIM systems. The accuracy and reliability are enabled through an automated RISC-V controlled on-chip calibration, allowing us to improve the compute SNR by 25 to 45% across multiple columns to reach 18-24 dB. To showcase further integration possibilities, we show how our proof-of-concept SoC can be extended to recent high-density linear resistor technologies for enhanced computing performance. Comments: This work has been submitted to the IEEE for possible publication. 12 pages, 10 figures, 2 tables Subjects: Hardware Architecture (cs.AR) Cite as: arXiv:2506.15440 [cs.AR] (or arXiv:2506.15440v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2506.15440 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1109/TCAD.2026.3682622 Focus to learn more DOI(s) linking to related resources Submission history From: Omar Numan [ view email ] [v1] Wed, 18 Jun 2025 13:14:04 UTC (13,463 KB) Full-text links: Access Paper: View a PDF of the paper titled Acore-CIM: build accurate and reliable mixed-signal CIM cores with RISC-V controlled self-calibration, by Omar Numan and 8 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AR < prev | next > new | recent | 2025-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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