The trend of unsustainable power consumption and large memory bandwidth demands in massively parallel multicore systems, with the advent of the big data era, has brought upon the onset of alternate computation paradigms utilizing heterogeneity, specialization, processor-in-memory and approximation. Approximate Computing is being touted as a viable solution for high performance computation by relaxing the accuracy constraints of applications. This trend has been accentuated by emerging data intensive applications in domains like image/video processing, machine learning and big data analytics that allow inaccurate outputs within an acceptable variance. Leveraging relaxed accuracy for high throughput in Networks-on-Chip (NoCs), which have rapidly become the accepted method for connecting a large number of on-chip components, has not yet been explored. We propose APPROX-NoC, a hardware data approximation framework with an online data error control mechanism for high performance NoCs. APPROX-NoC facilitates approximate matching of data patterns, within a controllable value range, to compress them thereby reducing the volume of data movement across the chip.Our evaluation shows that APPROX-NoC achieves on average up to 9% latency reduction and 60% throughput improvement compared with state-of-the-art NoC data compression mechanisms, while maintaining low application error. Additionally, with a data intensive graph processing application we achieve a 36.7% latency reduction compared to state-of-the-art compression mechanisms.
