7 papers
VitaLLM: A Versatile, Ultra-Compact Ternary LLM Accelerator with Dependency-Aware Scheduling
Zi-Wei Lin, Tian-Sheuan Chang
Deploying Large Language Models (LLMs) on resource-constrained edge devices faces critical bottlenecks in memory bandwidth and power consumption. While ternary quantization (e.g.,…
RCW-CIM: A Digital CIM-based LLM Accelerator with Read-Compute/Write
Yan-Cheng Guo, Tian-Sheuan Chang, Jian-Wei Su
Digital computing-in-memory (DCIM) has emerged as a promising solution for large language model (LLM) acceleration by minimizing data transfers between external DRAM and on-chip ac…
A 129FPS Full HD Real-Time Accelerator for 3D Gaussian Splatting
Fang-Chi Chang, Tian-Sheuan Chang
Rendering large-scale, unbounded scenes on AR/VR-class devices is constrained by the computation, bandwidth, and storage cost of 3D Gaussian Splatting (3DGS). We propose a low-powe…
Enhancing Finite State Machine Design Automation with Large Language Models and Prompt Engineering Techniques
Qun-Kai Lin, Cheng Hsu, Tian-Sheuan Chang
Large Language Models (LLMs) have attracted considerable attention in recent years due to their remarkable compatibility with Hardware Description Language (HDL) design. In this pa…
CIMR-V: An End-to-End SRAM-based CIM Accelerator with RISC-V for AI Edge Device
Yan-Cheng Guo and, Tian-Sheuan Chang, Chih-Sheng Lin +5
Computing-in-memory (CIM) is renowned in deep learning due to its high energy efficiency resulting from highly parallel computing with minimal data movement. However, current SRAM-…
VESTA: A Versatile SNN-Based Transformer Accelerator with Unified PEs for Multiple Computational Layers
Ching-Yao Chen, Meng-Chieh Chen, Tian-Sheuan Chang
Spiking Neural Networks (SNNs) and transformers represent two powerful paradigms in neural computation, known for their low power consumption and ability to capture feature depende…
Dynamic Gradient Sparse Update for Edge Training
I-Hsuan Li, Tian-Sheuan Chang
Training on edge devices enables personalized model fine-tuning to enhance real-world performance and maintain data privacy. However, the gradient computation for backpropagation i…