Researchers Find New Way to Speed Up Matrix Math in AI Chips
Scientists have developed faster algorithms for a mathematical operation critical to AI and wireless systems, testing them with simulated hardware constraints. The advance could lead to more efficient chip designs for machine learning applications—potentially lowering costs and power consumption in data centers and mobile devices.
Originaltitel: Finite Word-Length Effects for Symmetric Matrix Inversion with Vector-Based Algorithms
<p>In this work, we present an investigation of the numerical properties of different symmetric matrix inversion algorithms, with a focus on two vector-based algorithms: Block LDLT and Tile LDLT. The inversion of symmetric positive-definite matrices finds applications in many different areas of signal processing, such as in MIMO detection and adaptive filtering. Using arithmetic simulations with the APyTypes Python software library, we demonstrate the numerical properties of different algorithms when using fixed-point number representations. The two algorithms in focus show potential, performing very close to the standard LDLT algorithm. The Tile LDLT algorithm slightly outperforms the Block LDLT algorithm in most cases, especially for larger matrix sizes. The application-specific hardware architecture for the two matrix inversion algorithms is only given at a high level, with the actual implementation left as future work.</p>