A Technique for Approximate Communication in Network-on-Chips for Image Classification

Approximation is an emerging design methodology for reducing power consumption and latency of on-chip communication in many computing applications. However, existing approximation techniques either achieve modest improvements in these metrics or require retraining after approximation. Since classifying many images introduces intensive on-chip communication, reductions in both network latency and power consumption are highly desired. In this paper, we propose an approximate communication technique (ACT) to improve the efficiency of on-chip communications for image classification applications. The proposed technique exploits the error-tolerance of the image classification process to reduce power consumption and latency of on-chip communications, resulting in better overall performance for image classification. This is achieved by incorporating novel quality control and data approximation mechanisms that reduce the packet size. In particular, the proposed quality control mechanisms identify the error-resilient variables and automatically adjust the error thresholds of the variables based on the image classification accuracy. The proposed data approximation mechanisms significantly reduce packet size when the variables are transmitted. The proposed technique reduces the number of flits in each data packet as well as the on-chip communication while maintaining an excellent image classification accuracy. Cycle-accurate simulation results show that ACT achieves 23% in network latency reduction and 24% in dynamic power reduction as compared to existing approximate communication techniques with less than 0.99% classification accuracy loss.

Automated summary

In this paper, we propose an approximate communication technique (ACT) for image classification applications, which leverages the error-tolerance of the classification process to reduce power consumption and latency of on-chip communications. The proposed technique incorporates quality control and data approximation mechanisms to reduce packet size, achieving a reduction in network latency of 23% and dynamic power of 24% compared to existing approaches, with less than 0.99% classification accuracy loss.