Saved in:
| Main Authors: | Lin, Kuan-Ting, Chiu, Ching-Te, Chang, Jheng-Yi, Huang, Shi-Zong, Li, Yu-Ting |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.05688 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow
by: Hsiung, Ching-Lin, et al.
Published: (2025)
by: Hsiung, Ching-Lin, et al.
Published: (2025)
A Flexible Precision Scaling Deep Neural Network Accelerator with Efficient Weight Combination
by: Zhao, Liang, et al.
Published: (2025)
by: Zhao, Liang, et al.
Published: (2025)
A Hybrid-Domain Floating-Point Compute-in-Memory Architecture for Efficient Acceleration of High-Precision Deep Neural Networks
by: Yi, Zhiqiang, et al.
Published: (2025)
by: Yi, Zhiqiang, et al.
Published: (2025)
Energy-Efficient FPGA Framework for Non-Quantized Convolutional Neural Networks
by: Athanasiadis, Angelos, et al.
Published: (2025)
by: Athanasiadis, Angelos, et al.
Published: (2025)
Special Session: Sustainable Deployment of Deep Neural Networks on Non-Volatile Compute-in-Memory Accelerators
by: Qin, Yifan, et al.
Published: (2025)
by: Qin, Yifan, et al.
Published: (2025)
Work-in-Progress: Real-Time Neural Network Inference on a Custom RISC-V Multicore Vector Processor
by: Kirschner, Maximilian, et al.
Published: (2024)
by: Kirschner, Maximilian, et al.
Published: (2024)
VitaLLM: A Versatile and Tiny Accelerator for Mixed-Precision LLM Inference on Edge Devices
by: Lin, Zi-Wei, et al.
Published: (2026)
by: Lin, Zi-Wei, et al.
Published: (2026)
Exploring Quantization and Mapping Synergy in Hardware-Aware Deep Neural Network Accelerators
by: Klhufek, Jan, et al.
Published: (2024)
by: Klhufek, Jan, et al.
Published: (2024)
Hardware Acceleration of Kolmogorov-Arnold Network (KAN) for Lightweight Edge Inference
by: Huang, Wei-Hsing, et al.
Published: (2024)
by: Huang, Wei-Hsing, et al.
Published: (2024)
VitaLLM: A Versatile, Ultra-Compact Ternary LLM Accelerator with Dependency-Aware Scheduling
by: Lin, Zi-Wei, et al.
Published: (2026)
by: Lin, Zi-Wei, et al.
Published: (2026)
FILCO: Flexible Composing Architecture with Real-Time Reconfigurability for DNN Acceleration
by: Chen, Xingzhen, et al.
Published: (2026)
by: Chen, Xingzhen, et al.
Published: (2026)
FLICKER: A Fine-Grained Contribution-Aware Accelerator for Real-Time 3D Gaussian Splatting
by: Ou, Wenhui, et al.
Published: (2026)
by: Ou, Wenhui, et al.
Published: (2026)
TENET: An Efficient Sparsity-Aware LUT-Centric Architecture for Ternary LLM Inference On Edge
by: Huang, Zhirui, et al.
Published: (2025)
by: Huang, Zhirui, et al.
Published: (2025)
always_comm: An FPGA-based Hardware Accelerator for Audio/Video Compression and Transmission
by: Parthasarathy, Rishab, et al.
Published: (2025)
by: Parthasarathy, Rishab, et al.
Published: (2025)
SpikeStream: Accelerating Spiking Neural Network Inference on RISC-V Clusters with Sparse Computation Extensions
by: Manoni, Simone, et al.
Published: (2025)
by: Manoni, Simone, et al.
Published: (2025)
VESTA: A Versatile SNN-Based Transformer Accelerator with Unified PEs for Multiple Computational Layers
by: Chen, Ching-Yao, et al.
Published: (2025)
by: Chen, Ching-Yao, et al.
Published: (2025)
HURRY: Highly Utilized, Reconfigurable ReRAM-based In-situ Accelerator with Multifunctionality
by: Shin, Hery, et al.
Published: (2024)
by: Shin, Hery, et al.
Published: (2024)
DCI: A Coordinated Allocation and Filling Workload-Aware Dual-Cache Allocation GNN Inference Acceleration System
by: Luo, Yi, et al.
Published: (2025)
by: Luo, Yi, et al.
Published: (2025)
Hardware-Aware Neural Network Compilation with Learned Optimization: A RISC-V Accelerator Approach
by: Ganti, Ravindra, et al.
Published: (2025)
by: Ganti, Ravindra, et al.
Published: (2025)
A PVT-Resilient Subthreshold SRAM-Based In-Memory Computing Accelerator with In-Situ Regulation for Energy-Efficient Spiking Neural Networks
by: Kao, Shih-Hang, et al.
Published: (2026)
by: Kao, Shih-Hang, et al.
Published: (2026)
Real-Time, Energy-Efficient, Sampling-Based Optimal Control via FPGA Acceleration
by: Desai, Tanmay, et al.
Published: (2026)
by: Desai, Tanmay, et al.
Published: (2026)
3DGauCIM: Accelerating Static/Dynamic 3D Gaussian Splatting via Digital CIM for High Frame Rate Real-Time Edge Rendering
by: Huang, Wei-Hsing, et al.
Published: (2025)
by: Huang, Wei-Hsing, et al.
Published: (2025)
GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design
by: You, Haoran, et al.
Published: (2021)
by: You, Haoran, et al.
Published: (2021)
Analysis of Single Event Induced Bit Faults in a Deep Neural Network Accelerator Pipeline
by: Jonckers, Naïn, et al.
Published: (2025)
by: Jonckers, Naïn, et al.
Published: (2025)
TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference Acceleration Using Gradient Boosted Decision Trees
by: Khataei, Alireza, et al.
Published: (2025)
by: Khataei, Alireza, et al.
Published: (2025)
NeuroBlend: Towards Low-Power yet Accurate Neural Network-Based Inference Engine Blending Binary and Fixed-Point Convolutions
by: Fayyazi, Arash, et al.
Published: (2023)
by: Fayyazi, Arash, et al.
Published: (2023)
ARMAN: A Reconfigurable Monolithic 3D Accelerator Architecture for Convolutional Neural Networks
by: Sedaghatgoo, Ali, et al.
Published: (2024)
by: Sedaghatgoo, Ali, et al.
Published: (2024)
Sparse-on-Dense: Area and Energy-Efficient Computing of Sparse Neural Networks on Dense Matrix Multiplication Accelerators
by: Yoon, Hyunsung, et al.
Published: (2026)
by: Yoon, Hyunsung, et al.
Published: (2026)
DIRC-RAG: Accelerating Edge RAG with Robust High-Density and High-Loading-Bandwidth Digital In-ReRAM Computation
by: Shao, Kunming, et al.
Published: (2025)
by: Shao, Kunming, et al.
Published: (2025)
Convolutions Predictable Offloading to an Accelerator: Formalization and Optimization
by: Husson, Benjamin, et al.
Published: (2026)
by: Husson, Benjamin, et al.
Published: (2026)
Energy-Aware Heterogeneous Federated Learning via Approximate DNN Accelerators
by: Pfeiffer, Kilian, et al.
Published: (2024)
by: Pfeiffer, Kilian, et al.
Published: (2024)
HSCO-Bench: An Agent-Driven End-to-End Hardware-Software Co-design Benchmark for Systems-on-Chip
by: Tsai, Pei-Huan, et al.
Published: (2026)
by: Tsai, Pei-Huan, et al.
Published: (2026)
Voxel-CIM: An Efficient Compute-in-Memory Accelerator for Voxel-based Point Cloud Neural Networks
by: Lin, Xipeng, et al.
Published: (2024)
by: Lin, Xipeng, et al.
Published: (2024)
Lumina: Real-Time Mobile Neural Rendering by Exploiting Computational Redundancy
by: Feng, Yu, et al.
Published: (2025)
by: Feng, Yu, et al.
Published: (2025)
A Switch-Centric In-Network Architecture for Accelerating LLM Inference in Shared-Memory Network
by: Jiang, Aojie, et al.
Published: (2026)
by: Jiang, Aojie, et al.
Published: (2026)
FireFly-P: FPGA-Accelerated Spiking Neural Network Plasticity for Robust Adaptive Control
by: Li, Tenglong, et al.
Published: (2026)
by: Li, Tenglong, et al.
Published: (2026)
Hybrid Systolic Array Accelerator with Optimized Dataflow for Edge Large Language Model Inference
by: Chen, Chun-Ting, et al.
Published: (2025)
by: Chen, Chun-Ting, et al.
Published: (2025)
OPIMA: Optical Processing-In-Memory for Convolutional Neural Network Acceleration
by: Sunny, Febin, et al.
Published: (2024)
by: Sunny, Febin, et al.
Published: (2024)
Architecture-Level Modeling of Photonic Deep Neural Network Accelerators
by: Andrulis, Tanner, et al.
Published: (2024)
by: Andrulis, Tanner, et al.
Published: (2024)
Monitor Placement for Fault Localization in Deep Neural Network Accelerators
by: Liu, Wei-Kai
Published: (2023)
by: Liu, Wei-Kai
Published: (2023)
Similar Items
-
Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow
by: Hsiung, Ching-Lin, et al.
Published: (2025) -
A Flexible Precision Scaling Deep Neural Network Accelerator with Efficient Weight Combination
by: Zhao, Liang, et al.
Published: (2025) -
A Hybrid-Domain Floating-Point Compute-in-Memory Architecture for Efficient Acceleration of High-Precision Deep Neural Networks
by: Yi, Zhiqiang, et al.
Published: (2025) -
Energy-Efficient FPGA Framework for Non-Quantized Convolutional Neural Networks
by: Athanasiadis, Angelos, et al.
Published: (2025) -
Special Session: Sustainable Deployment of Deep Neural Networks on Non-Volatile Compute-in-Memory Accelerators
by: Qin, Yifan, et al.
Published: (2025)