arXiv AI

CRAM-ER: Error-Resilient Spintronic Computational Random Access Memory for Scalable In-Memory Computation

arXiv:2606. 02781v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have achieved state-of-the-art performance across diverse domains.

arXiv AI

Jun 9

Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications

arXiv:2605. 11855v2 Announce Type: replace-cross Abstract: Sequence learning is dominated by Transformers and parallelizable recurrent neural networks (RNNs) such as state-space models, yet learning long-term dependencies remains challenging, and state-of-the-art designs trade power consumption for performance.

By Julien Brandoit, Arthur Fyon, Damien Ernst, Guillaume Drion

llms efficiency benchmarks

arXiv Machine Learning

Aug 6

SHIELD: A Segmented Hierarchical Memory Architecture for Energy-Efficient LLM Inference on Edge NPUs

arXiv:2604. 07396v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference on edge Neural Processing Units (NPUs) is fundamentally constrained by limited on-chip memory capacity.

By Jintao Zhang, Xuanyao Fong

llms

arXiv AI

Jul 28

The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing

arXiv:2607. 24396v1 Announce Type: cross Abstract: In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications.

By Stefan Scholze, Johannes Partzsch, Sebastian H\"oppner, Florian Kelber, Andreas Dixius, Marco Stolba, Sirine Arfa, Marc Berthel, Georg Ellguth, Jim Garside, Hector A. Gonzalez, Stephan Hartmann, Thomas Kiel-Hocker, Dongwei Hu, Matthias Jobst, Khaleelulla Khan Nazeer, Tim Langer, Chen Liu, Gengting Liu, Matthias Lohrmann, Mantas Mikaitis, Felix Neum\"arker, Amirhossein Rostami, Stefan Schiefer, Tilo Schubert, Delong Shang, Bernhard Vogginger, Yexin Yan, Steve Furber, Christian Mayr

arXiv AI

Jul 17

NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference

arXiv:2607. 15123v1 Announce Type: cross Abstract: Recent FPGAs have improved deep learning (DL) inference efficiency through dedicated tensor blocks and in-BRAM computation.

By Jiajun Hu, Ruthwik Reddy Sunketa, Lei Zhao, Archit Gajjar, Luca Buonanno, Aman Arora

llms benchmarks

arXiv Machine Learning

Jun 8

OpenACMv2: An Accuracy-Constrained Co-Optimization Framework for Approximate DCiM

arXiv:2603. 13042v2 Announce Type: replace Abstract: Digital Compute-in-Memory (DCiM) accelerates neural networks by reducing data movement.

By Yiqi Zhou, Yue Yuan, Yikai Wang, Bohao Liu, Qinxin Mei, Zhuohua Liu, Shan Shen, Wei Xing, Daying Sun, Li Li, Guozhu Liu

arXiv Machine Learning

Jul 16

Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer

arXiv:2505. 14303v3 Announce Type: replace-cross Abstract: Using Resistive Random Access Memory (RRAM) crossbars in Computing-in-Memory (CIM) architectures offers a promising solution to overcome the von Neumann bottleneck.

By Rebecca Pelke, Jos\'e Cubero-Cascante, Nils Bosbach, Niklas Degener, Florian Idrizi, Lennart M. Reimann, Jan Moritz Joseph, Rainer Leupers

efficiency

arXiv AI

Aug 10

Predictive Multi-Tier Memory Management for KV Cache in Large-Scale GPU Inference

arXiv:2604. 26968v2 Announce Type: replace-cross Abstract: Key-value (KV) cache memory management is the primary bottleneck limiting throughput and cost-efficiency in large-scale GPU inference serving.

By Sanjeev Rao Ganjihal

agents efficiency

arXiv Machine Learning

Jul 21

ThRIve: Thermally Robust CNN Inference via Low-Rank Adaptation in Heterogeneous PIM Architectures

arXiv:2607. 17091v1 Announce Type: cross Abstract: Processing-In-Memory (PIM) has emerged as a promising technology for accelerating machine learning (ML) workloads.

By Vibhanshu Sharma, Pratyush Dhingra, Janardhan Rao Doppa, Partha Pratim Pande

arXiv AI

Aug 11

SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks

arXiv:2608. 08479v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing.

By Prasanna Date, Kevin Zhu, Shruti Kulkarni, Ashish Gautam, Chathika Gunaratne, Robert Patton, Tyler Nitzsche, Ian Mulet, Zachary Johnson-Scott, Addison Helms, Duncan Rowden, Simon Weston, Maryam Parsa, Catherine Schuman, Thomas Potok

benchmarks

arXiv Machine Learning

Jun 17

ReRAM-aware Model Finetuning addressing I-V Non-linearity and Retention Errors

arXiv:2606. 17471v1 Announce Type: new Abstract: Traditional CPU, GPU, and NPU architectures are increasingly limited by the von Neumann bottleneck.

By Ching-Yi Lin, Shamik Kundu, Arnab Raha, Sahil Shah

computer-vision fine-tuning

arXiv AI

Jun 3

MX-SAFE: Versatile Inference- and Training-Proof Microscaling Format with On-the-Fly Exponent and Mantissa Bit Allocation

arXiv:2605. 24391v2 Announce Type: replace-cross Abstract: As the demand for deep learning grows, cost reduction through quantization has become essential for both training and inference.

By Dahoon Park, Jahyun Koo, Sangwoo Hwang, Jaeha Kung

efficiency

arXiv Machine Learning

Jul 30

LLMET: Enabling Cross-Layer Evaluation of Emerging M3D Memories for Energy-Efficient LLM Serving

arXiv:2607. 26491v1 Announce Type: cross Abstract: The energy consumption of Large Language Model (LLM) serving is becoming a major system challenge as deployment scales, driven by hardware power and thermal constraints and rising electricity costs.

By Ming-Yen Lee, Hanchen Yang, Faaiq Waqar, Harsono Simka, Tushar Krishna, Muhammed Ahosan Ul Karim, Shimeng Yu

llms

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