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HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference

arXiv:2609.00450v1 Announce Type: cross Abstract: Block Quantization (BQ) is a promising approach for efficient deployment of large language models (LLMs), enabling low-precision computation with cont

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2 Sept 2026

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Enabling AI ASICs for Zero Knowledge Proof

Smart Timing for Mining: A Deep Learning Framework for Bitcoin Hardware ROI Prediction

ITP-STDP: An Intrinsic-Timing Power-of-Two Learning Engine for On-Chip SNN Training

Cross-Layer Co-Optimized LSTM Accelerator for Real-Time Gait Analysis

FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing

Security-Enhanced Seed-Based Weight Quantization for Large Language Models

EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review

Hardware-Aware Learned Representation Compression for Distributed In-Sensor Vision

HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference

BitLogic: Training Framework for Gradient-Based FPGA-Native Neural Networks

DBNN: Neural Spike Classification Using a Deep Binarized Neural Network

SPARX: Secure and Privacy-Aware Approximate CNN Acceleration with Edge RISC-V SoC

SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction

AMS-HD: Hyperdimensional Computing for Real-Time and Energy-Efficient Acute Mountain Sickness Detection

CARMEN: CORDIC-Accelerated Resource-Efficient Multi-Precision Inference Engine for Deep Learning

RainFusion2.0: Temporal-Spatial Awareness and Hardware-Efficient Block-wise Sparse Attention

SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification