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…
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-…
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…
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…