Neural Network Hardware Acceleration

Latest papers

37 papers7 papers in the last four weeks, up 40% on the four weeks before. 0.0% of all new papers.

Small-scale neural simulation of Caenorhabditis elegans on Loihi-2 using NeuroML2Loihi
Oct 18, 2026Danny Rosen, Ramashish Gaurav, Yang YiNeuromorphic Computing Neural Network Hardware Acceleration
Virginia Tech, Institute for Advanced Computing · Institute for Advanced Computing, Virginia Polytechnic Institute and State University, Alexandria, Virginia, USA
First-Order LoRA Training with Phone NPUs
Oct 17, 2026Xu Zheng, Wei Cheng, Zhuomin Chen +4On-Device Training LLM Fine-Tuning
Florida International University, Miami, FL, USA · NEC Laboratories America, Princeton, NJ, USA · North Carolina State University, Raleigh, NC, USA +2
TorchTPU - A Hybrid Eager/Compiled Architecture for Native PyTorch Execution on TPU
Oct 16, 2026Claudio Basile, Michael Voznesensky, Gunhyun Park +50Neural Network Hardware Acceleration Efficient Neural Network Inference
Hardware-aware Calibrated Clustered Attention for Efficient Visual Geometric Transformers
Oct 7, 2026Weitian Wang, Shubham Rai, Cecilia De La Parra +1Neural Network Hardware Acceleration Efficient ViTs
Robert Bosch GmbH, Germany · Ruhr University Bochum, Germany
Beyond Masked Sparsity: SNACK Enables Truly Sparse Neural Networks on GPU
Oct 2, 2026Jafar Badour, Maurice van Keulen, Elena MocanuDynamic Sparse Training Sparse Neural Networks
University of Twente
LampAttention: Look-Ahead Mixed-Precision FlashAttention for Dedicated Accelerators
Sep 30, 2026Stanislav Budzinskiy, Marian Gloser, Tolunay Yilmaz +5AI Accelerator Inference LLM Inference Acceleration
Faculty of Mathematics University of Vienna, Austria · Huawei Heisenberg Research Center, Munich, Germany · Huawei Technologies Co. Ltd
KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization
Sep 24, 2026Aheli Poddar, Sanskar Prasad, Arindam Samanta +3GPU Kernel Optimization Agentic Search
Red Hat
RISC-V and machine learning: a survey
Sep 17, 2026Shriman Keshri, Apparna Singh, Chinmaya Kumar Palo +2Energy-Efficient ML Neural Network Hardware Acceleration
National Institute of Science Education and Research, Bhubaneswar, Odisha, India · Work conducted at NISER · Sri Sri University, Cuttack, Odisha, India +2
NeuroFlex: Lossless Element-Level ANN-SNN Co-Execution for Efficient Sparse Inference
Sep 12, 2026Varun Manjunath, Pranav Ramesh, Gopalakrishnan SrinivasanEfficient Neural Network Inference AI Accelerator Inference
Electrical Engineering Indian Institute of Technology Madras Chennai, India · Computer Science and Engineering Indian Institute of Technology Madras Chennai, India
Fast Polynomial Transcendentals for LLMs
Sep 3, 2026Robert HuGPU Kernel Optimization Neural Network Hardware Acceleration
GRAPHCORE RESEARCH
Hardware-Accelerated Instance Segmentation for Resource-Constrained Space Robotics with Criticality Analysis
Sep 2, 2026Siddhant Shete, Hilmi Dogu Kücüker, Udo Frese +1Neural Network Hardware Acceleration Instance Segmentation
Robotics Innovation Center of the German Research Center for Artificial Intelligence GmbH (DFKI), Bremen, Germany · University of Bremen, Germany
Redwood: A Frontier AI Accelerator Designed, Verified, and Deployed from Scratch in 2 Weeks by AI
Aug 26, 2026Architect LabsAI Accelerator Inference Neural Network Hardware Acceleration
Architect Labs Team* Palo Alto, California
BaseRT: Advancing Best-in-Class LLM Inference with Apple M5 Neural Accelerators
Jul 21, 2026Fabian Waschkowski, Prabod Rathnayaka, Lukas WesemannAI Accelerator Inference On-Device Language Model Inference
Base Compute, Melbourne, Australia
NKI-Agent: Domain-Specific Fine-Tuning and Agentic Tool Use for Neuron Kernel Generation
Jul 5, 2026Junjie Tang, Jun Huan, Hao Zhou +2Supervised Fine-Tuning AI Coding Agents
Amazon Web Services
BluTrain: A C++/CUDA Framework for AI Systems
Jun 23, 2026Adhitya Charan, Adwaid Suresh, Anuj Kumar +19GPU Acceleration High-Performance Computing
Blubridge AI
MIVE: A Minimalist Integer Vector Engine for Softmax LayerNorm and RMSNorm Acceleration
Jun 16, 2026Kosmas Alexandridis, Giorgos DimitrakopoulosAI Accelerator Inference Inference Acceleration
Integrated Circuits Lab, Electrical and Computer Engineering, Democritus University of Thrace (DUTH), Greece
ANEForge: Python for direct computation on the Apple Neural Engine
Jun 12, 2026Spencer H. BryngelsonEfficient Neural Network Inference AI Accelerator Inference
School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA · Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA · George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
AutoMegaKernel: A Statically-Checked Agent Harness for Self-Retargeting Megakernel Synthesis
Jun 8, 2026Jaber Jaber, Osama JaberGPU Kernel Optimization High-Performance Computing
RightNow AI
MelT: A Portable, Single-GEMM Mel Audio Frontend via Non-Uniform DFT with Measured Latency and Energy Gains on GPUs
May 31, 2026Augusto Camargo, Marcelo FingerGPU Acceleration Speech Processing
Institute of Mathematics and Statistics (IME), University of São Paulo, São Paulo, Brazil
ORBIS: Output-Guided Token Reduction with Distribution-Aware Matching for Video Diffusion Acceleration
May 21, 2026Hangyeol Lee, Joo-Young KimToken Merging Video Diffusion Models
KAIST · Daejeon, South Korea
FLARE: One-Shot PE-Level Fault Localization in Systolic Arrays via Algebraic Test Vectors
May 9, 2026Logashree Venkatasubramanian, Zishen Wan, Viveck CadambeAI Accelerator Inference Neural Network Hardware Acceleration
Georgia Institute of Technology · Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta, Georgia, USA
TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators
May 6, 2026Chang Meng, Hanyu Wang, Yuyang Ye +3AI Accelerator Inference Energy-Efficient ML
Eindhoven University of Technology Eindhoven, The Netherlands · University of California, Los Angeles Los Angeles, USA · Chinese University of Hong Kong Hong Kong, China +2
Hardware-Efficient Softmax and Layer Normalization with Guaranteed Normalization for Edge Devices
Apr 26, 2026Dawon Choi, Hana Kim, Ji-Hoon KimEfficient Neural Network Inference Layer Normalization
Dept. of Artificial Intelligence Semiconductor Engineering, Hanyang University, Seoul, Republic of Korea · Dept. of Electronic Engineering, Hanyang University, Seoul, Republic of Korea
HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference
Apr 24, 2026Chang Sun, Zhiqiang Que, Bakhtiar Zadeh +4Efficient Neural Network Inference FPGA-Based Neural Network Acceleration
California Institute of Technology, Pasadena, CA, USA · University of Bristol, UK · Imperial College London, UK +1
A-IO: Adaptive Inference Orchestration for Memory-Bound NPUs
Apr 10, 2026Chen Zhang, Yan Ding, Haotian Wang +3LLM Inference Acceleration LLM Inference Scheduling
Hunan University Changsha, China
MATCHA: Efficient Deployment of Deep Neural Networks on Multi-Accelerator Heterogeneous Edge SoCs
Apr 10, 2026Enrico Russo, Mohamed Amine Hamdi, Alessandro Ottaviano +6Efficient Neural Network Inference Neural Network Hardware Acceleration
University of Catania, Italy · Politecnico di Torino, Italy · ETH Zurich, Switzerland +1
A Synthesizable RTL Implementation of Predictive Coding Networks
Mar 18, 2026Timothy OhNeural Network Hardware Acceleration Predictive Coding
University of California, Riverside
Collapse or Preserve: Data-Dependent Temporal Aggregation for Spiking Neural Network Acceleration
Mar 14, 2026Jiahao QinSpiking Neural Networks Efficient Neural Network Inference
Hardware Efficient Approximate Convolution with Tunable Error Tolerance for CNNs
Mar 10, 2026Vishal Shashidhar, Anupam Kumari, Roy P PailyEfficient Neural Network Inference Neural Network Hardware Acceleration
Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati
Orion: Characterizing and Programming Apple's Neural Engine for LLM Training and Inference
Mar 6, 2026Ramchand KumaresanNeural Network Hardware Acceleration On-Device Training

Oct 18, 2026J/K move · Enter open · S save

Small-scale neural simulation of Caenorhabditis elegans on Loihi-2 using NeuroML2Loihi

Danny Rosen, Ramashish Gaurav, Yang Yi

Virginia Tech, Institute for Advanced Computing · Institute for Advanced Computing, Virginia Polytechnic Institute and State University, Alexandria, Virginia, USA

Neuromorphic Computing Neural Network Hardware Acceleration

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Neural simulation is an essential methodology for studying biological nervous systems, but it remains limited by the computational costs of repeatedly updating neural and synaptic states over many timesteps. Neuromorphic hardware is a promising substrate for this class of workloads because such devices combine event-driven execution, localized memory, and spike-based communication. However, neuromorphic software remains comparatively fragmented and difficult to integrate with the model-description standards used by the computational neuroscience community. This disconnect limits reproducible experimentation and makes direct comparison between conventional and neuromorphic simulation workflows difficult. This paper introduces ``NeuroML2Loihi'', an open-source framework for translating NeuroML model descriptions into Loihi~~2-deployable Lava primitives, enabling standardized biological models to be executed on Intel's Loihi~~2 neuromorphic chip. We evaluate our framework using c302-derived C. elegans neural models across three connectome-scale configurations. Cross-platform similarity is assessed using spike-train and voltage-trace metrics, while latency, power, and energy consumption are benchmarked against the conventional reference workflow. Across the evaluated models, Loihi2 execution enabled through NeuroML2Loihi achieves 514--1211 $\times$ lower latency and a 1.8--1.9 $\times$ reduction in mean wattage relative to the NEURON reference, producing substantially lower total energy consumption primarily through reduced execution time. These results demonstrate that standards-based neuromorphic neural simulation is feasible on Loihi2 and can support meaningful comparison against established computational neuroscience workflows.