arXiv subjects

Feng Min

Publications and source records attributed to Feng Min.

3 recordsLinked to original sources

arXiv2024

Adaptive Soft Error Protection for Neural Network Processing

Previous research on selective protection for neural network components typically exploits only static vulnerability differences. Although these methods improve upon classical modular redundancy, they still incur substantial overhead for neural network workloads that are both memory-intensive and compute-intensive. In this work, we observe that neural network vulnerability is also input-dependent and varies dynamically at runtime. With this observation, we propose an adaptive, vulnerability-aware fault tolerance framework. At its core, a lightweight graph neural network (GNN) model dynamically predicts soft error vulnerabilities across inputs and neural network components, enabling real-time adaptation of fault tolerance policies. This design offers a complementary and more efficient protection scheme compared to traditional approaches. Experimental results demonstrate that the GNN predictor achieves over 95% accuracy in identifying critical inputs and components. Moreover, our adaptive scheme reduces computational overhead by an average of 42.12% while preserving model accuracy, significantly outperforming static selective protection methods.

cs.LG↗

arXiv2024

DaDu-Corki: Algorithm-Architecture Co-Design for Embodied AI-powered Robotic Manipulation

Embodied AI robots have the potential to fundamentally improve the way human beings live and manufacture. Continued progress in the burgeoning field of using large language models to control robots depends critically on an efficient computing substrate, and this trend is strongly evident in manipulation tasks. In particular, today's computing systems for embodied AI robots for manipulation tasks are designed purely based on the interest of algorithm developers, where robot actions are divided into a discrete frame basis. Such an execution pipeline creates high latency and energy consumption. This paper proposes \textsc{Corki}\xspace, an algorithm-architecture co-design framework for real-time embodied AI-powered robotic manipulation applications. We aim to decouple LLM inference, robotic control, and data communication in the embodied AI robots' compute pipeline. Instead of predicting action for one single frame, \textsc{Corki}\xspace predicts the trajectory for the near future to reduce the frequency of LLM inference. The algorithm is coupled with a hardware that accelerates transforming trajectory into actual torque signals used to control robots and an execution pipeline that parallels data communication with computation. \textsc{Corki}\xspace largely reduces LLM inference frequency by up to $5.1\times$, resulting in up to $5.9\times$ speed up. The success rate improvement can be up to 13.9\%.

cs.AR↗

arXiv2023

ApproxABFT: Approximate Algorithm-Based Fault Tolerance for Neural Network Processing

With the increasing deployment of deep neural networks (DNNs) in terrestrial and aerospace safety-critical applications, system reliability has emerged as a co-equal design metric alongside computational efficiency. Algorithm-based fault tolerance (ABFT) mechanisms, characterized by architecture-agnostic and cost-effectiveness, have become a promising solution for reliability enhancement. However, conventional ABFT approaches rely on rigorous verification mechanisms where even minor computational deviations trigger error recovery processes, which not only disregards the intrinsic fault tolerance characteristics of DNN models but also incurs redundant fault tolerance processing overhead. To address these limitations, we propose an Approximate ABFT framework (ApproxABFT) that innovatively introduces adaptive error tolerance thresholds to enable selective fault recovery, activating error correction modules exclusively when computational deviations exceed predefined thresholds. This approach effectively mitigating overreaction to non-critical computational errors. Furthermore, a dynamic block granularity optimization algorithm is implemented to achieve inter-layer error sensitivity balancing. Experimental evaluations demonstrate that the proposed ApproxABFT achieves a 43.39% average reduction in redundant computing overhead compared to previous accurate ABFT, while simultaneously enhancing the tolerable soft error rate by an order of magnitude.

cs.CR↗