1 citations · 2 across the 6 of their papers we have counts for
6 papers
TOM: A Ternary Read-only Memory Accelerator for LLM-powered Edge Intelligence
Hongyi Guan, Yijia Zhang, Wenqiang Wang +4
The deployment of Large Language Models (LLMs) for real-time intelligence on edge devices is rapidly growing. However, conventional hardware architectures face a fundamental memory…
CCSS: Hardware-Accelerated RTL Simulation with Fast Combinational Logic Computing and Sequential Logic Synchronization
Weigang Feng, Yijia Zhang, Zekun Wang +4
As transistor counts in a single chip exceed tens of billions, the complexity of RTL-level simulation and verification has grown exponentially, often extending simulation campaigns…
ROMA: a Read-Only-Memory-based Accelerator for QLoRA-based On-Device LLM
Wenqiang Wang, Yijia Zhang, Zikai Zhang +4
As large language models (LLMs) demonstrate powerful capabilities, deploying them on edge devices has become increasingly crucial, offering advantages in privacy and real-time inte…
Automating Energy-Efficient GPU Kernel Generation: A Fast Search-Based Compilation Approach
Yijia Zhang, Zhihong Gou, Shijie Cao +4
Deep Neural Networks (DNNs) have revolutionized various fields, but their deployment on GPUs often leads to significant energy consumption. Unlike existing methods for reducing GPU…
BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation
Dayou Du, Yijia Zhang, Shijie Cao +4
The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges. Weight quantizati…
AFPQ: Asymmetric Floating Point Quantization for LLMs
Yijia Zhang, Sicheng Zhang, Shijie Cao +4
Large language models (LLMs) show great performance in various tasks, but face deployment challenges from limited memory capacity and bandwidth. Low-bit weight quantization can sav…