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Tag

#hardware

7 articles tagged #hardware

arxiv5d ago

Fault tolerance estimation in digital circuits with visualised generative networks

arXiv:2605.15212v2 Announce Type: replace-cross Abstract: We propose a new numerical method to estimate the fault tolerance of failure modes in digital circuit structures with a generative network sampling technique. From a random input of generated bitwise configurations of ideally digitalised anal

GE1 model#hardware#artificial-intelligence#circuit-designRead on arxiv →
arxivMay 6

A Synthesizable RTL Implementation of Predictive Coding Networks

arXiv:2603.18066v2 Announce Type: replace-cross Abstract: Backpropagation has enabled modern deep learning but is difficult to realize as an online, fully distributed hardware learning system due to global error propagation, phase separation, and heavy reliance on centralized memory. Predictive codi

#hardware#predictive-coding#neural-networksRead on arxiv →
arxivApr 29bullish

Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement

arXiv:2604.14989v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have sparked growing interest in automatic RTL optimization for better performance, power, and area (PPA). However, existing methods are still far from realistic RTL optimization. Their evaluation set

DR1 model#optimization#eda#rtlRead on arxiv →
arxivApr 27bullish

HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference

arXiv:2604.22293v1 Announce Type: cross Abstract: Lookup-table (LUT) based neural networks can deliver ultra-low latency and excellent hardware efficiency on FPGAs by mapping arithmetic operations directly onto the logic primitives. However, state-of-the-art LUT-aware training (LAT) approaches remai

HG1 model#hardware#efficiency#neural-networksRead on arxiv →
arxivApr 24bullish

ChipCraftBrain: Validation-First RTL Generation via Multi-Agent Orchestration

arXiv:2604.19856v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise for generating Register-Transfer Level (RTL) code from natural language specifications, but single-shot generation achieves only 60-65% functional correctness on standard benchmarks. Multi-agent approaches su

CHMACH4 models · +1#hardware#synthesis#generationRead on arxiv →
arxivApr 23bullish

DISCA: A Digital In-memory Stochastic Computing Architecture Using A Compressed Bent-Pyramid Format

arXiv:2511.17265v2 Announce Type: replace-cross Abstract: Nowadays, we are witnessing an Artificial Intelligence revolution that dominates the technology landscape in various application domains, such as healthcare, robotics, automotive, security, and defense. Massive-scale AI models, which mimic th

#hardware#artificial-intelligence#edge-computingRead on arxiv →
arxivApr 7bullish

Neuromorphic Computing for Low-Power Artificial Intelligence

arXiv:2604.04727v1 Announce Type: cross Abstract: Classical computing is beginning to encounter fundamental limits of energy efficiency. This presents a challenge that can no longer be solved by strategies such as increasing circuit density or refining standard semiconductor processes. The growing c

#neuromorphic#hardware#energy-efficiencyRead on arxiv →
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