The survey reviews how the open‑source RISC‑V ISA is being applied to machine learning, covering academic and commercial implementations, software frameworks, and real‑world applications. It presents a unified taxonomy of RISC‑V ML implementations, compares performance and design trade‑offs, evaluates toolchain maturity, and identifies emerging trends in instruction set extensions and specialized accelerators. The findings highlight progress in energy efficiency and framework integration, while noting challenges in standardization, verification, and ecosystem fragmentation, and propose four research directions to advance RISC‑V for next‑generation ML systems.
By Shriman Keshri, Apparna Singh, Chinmaya Kumar Palo, Shreya Adya, Subhankar Mishra
arXiv:2606. 19964v1 Announce Type: new Abstract: Tsetlin Machine (TM) is a logic-based machine learning approach that relies on simple bitwise operations and finite-state automata, which makes it attractive for edge AI deployments.
By Chanda Gupta, Sanidhya Bhatia, Shaurya Priyadarshi, Himani Panwar, Rishad Shafik, Sudip Roy
arXiv:2311. 17815v3 Announce Type: replace-cross Abstract: Given their increasing size and complexity, the need for efficient execution of deep neural networks has become increasingly pressing in the design of heterogeneous High-Performance Computing (HPC) and edge platforms, leading to a wide variety of proposals for specialized deep learning architectures and hardware accelerators.
By Serena Curzel, Fabrizio Ferrandi, Leandro Fiorin, Daniele Ielmini, Cristina Silvano, Francesco Conti, Luca Bompani, Luca Benini, Enrico Calore, Sebastiano Fabio Schifano, Cristian Zambelli, Maurizio Palesi, Giuseppe Ascia, Enrico Russo, Valeria Cardellini, Salvatore Filippone, Francesco Lo Presti, Stefania Perri
arXiv:2606. 17781v1 Announce Type: cross Abstract: The rapid growth of Large Language Models (LLMs) has intensified the need for specialized hardware accelerators that can satisfy stringent inference latency and power constraints.
By Kosmas Alexandridis, Giorgos Dimitrakopoulos
arXiv:2607. 06839v1 Announce Type: new Abstract: Existing NAS benchmarks (e.
By Tolgay Atinc Uzun, Waleed Khalid, Saif U Din, Sai Revanth Mulukuledu, Akashdeep Singh, Chandini Vysyaraju, Raghuvir Duvvuri, Avi Goyal, Yashkumar Rajeshbhai Lukhi, Muhammad A. Hussain, Krunal Jesani, Usha Shrestha, Yash Mittal, Roman Kochnev, Pritam Kadam, Mohsin Ikram, Harsh R. Moradiya, Alice Arslanian, Dmitry Ignatov, Radu Timofte
arXiv:2607. 05240v1 Announce Type: cross Abstract: Computing-in-Memory (CIM) accelerators execute Matrix-Vector Multiplications (MVMs) in memory, making them a compelling solution for Machine Learning (ML) workloads.
By Joel Klein, Rebecca Pelke, Roberto Laudani, Jan Moritz Joseph, Rainer Leupers