An Area-Efficient Systolic Array Redundancy Architecture for Reliable AI Accelerator - Lee, Hayoung; Park, Jongho; Kang, Sungho (IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2024)
The increasing demand for data-intensive analytics, driven by the rapid advances in artificial intelligence (AI), has led to the proposal of various AI accelerators. However, as AI-based solutions are being applied to applications that require high accuracy and reliability, ensuring the dependability of these solutions has become a critical issue. In this brief, we present an area-efficient systolic array redundancy architecture for reliable AI accelerator. In the proposed architecture, computations assigned to faulty multiply-accumulate (MAC) units are bypassed using dedicated routes. Subsequently, the same computations are executed in shiftable redundant MACs or selectable redundant MACs. This ensures the correct completion of calculations all without performance reduction. Moreover, the reassignment of computations can be efficiently managed through a simple scheduling algorithm. As a result, the proposed architecture achieves a high repair rate through the redundant MACs and effective computation reassignment. Despite these capabilities, the proposed architecture incurs only a small area overhead.
Keywords: Area-Efficient; Artificial intelligence; Artificial intelligence accelerator; Critical issues; Data intensive; High reliability; High-accuracy; Multiply-accumulate unit; Proposed architectures; Redundancy architectures; AI accelerator; Artificial intelligence (AI); redundancy; repair; systolic array