Presentation
LLM-based Soft Error tolerant Design for DNN accelerators
DescriptionRecent advancements in digital circuit manufacturing have enabled the widespread use of specialized chips, such as Deep Neural Network (DNN) accelerators, in safety-critical applications like autonomous driving and healthcare. Despite these advances, the reliability of these chips remains a concern due to soft errors from fabrication defects and radiation.Traditional soft error analysis approaches involve developing custom fault injection simulators for different data flows and hardware configurations, followed by extensive error injection experiments to inform fault-tolerant design strategies. Even with Electronic Design Automation (EDA) tools, achieving comprehensive coverage across models and hardware components is time- and resource-intensive.
The advent of Large Language Models (LLMs) presents a promising alternative. In this paper, we propose an iterative LLM-based framework to address soft errors in DNN accelerators. The framework integrates fault injection, Algorithm-Based Fault Tolerance (ABFT), and faulttolerant hardware architecture.Specifically, we introduce LEGA, a heuristic algorithm designed to enhance fault tolerance using LLM insights. The effectiveness of our framework is validated through extensive testing with 143 operators and various DNN workloads on DNN accelerators.
The advent of Large Language Models (LLMs) presents a promising alternative. In this paper, we propose an iterative LLM-based framework to address soft errors in DNN accelerators. The framework integrates fault injection, Algorithm-Based Fault Tolerance (ABFT), and faulttolerant hardware architecture.Specifically, we introduce LEGA, a heuristic algorithm designed to enhance fault tolerance using LLM insights. The effectiveness of our framework is validated through extensive testing with 143 operators and various DNN workloads on DNN accelerators.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby
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