研究目的
To autonomously organize heterogeneous resources to attain custom Computing Elements (CEs) for a range of transcendental mathematical functions with tunable accuracies.
研究成果
ECHELON was instrumental in approximating functions to the targeted 10% error limit for most of the functions evolved. Significant speedup in performance is achieved using ECHELON in comparison with standard approaches to perform similar computations. The results library by ECHELON may be used as a configuration store with minimal memory overhead for chromosomes of different computational circuits evolved to perform rapid computations of functions with increasing levels of complexity.
研究不足
The approach faces challenges of hardware-software co-design optimization, device signal range constraints, and limited precision. Additionally, the accuracy of the evolved functions is constrained by the device characteristics and the evolutionary algorithm's ability to minimize error.