Revamping Timing Error Resilience to Tackle Choke Points at NTC

The growing market of portable devices and smart wearables has contributed to innovation and development of systems with longer battery-life. While Near Threshold Computing (NTC) systems address the need for longer battery-life, they have certain limitations. NTC systems are prone to be significantly affected by variations in the fabrication process, commonly called process variation (PV). This dissertation explores an intriguing effect of PV, called choke points. Choke points are especially important due to their multifarious influence on the functional correctness of an NTC system. This work shows why novel research is required in this direction and proposes two techniques to resolve the problems created by choke points, while maintaining the reduced power needs

Revamping Timing Error Resilience to Tackle Choke Points at NTC

The growing market of portable devices and smart wearables has contributed to innovation and development of systems with longer battery-life. While Near Threshold Computing (NTC) systems address the need for longer battery-life, they have certain limitations. NTC systems are prone to be significantly affected by variations in the fabrication process, commonly called process variation (PV). This dissertation explores an intriguing effect of PV, called choke points. Choke points are especially important due to their multifarious influence on the functional correctness of an NTC system. This work shows why novel research is required in this direction and proposes two techniques to resolve the problems created by choke points, while maintaining the reduced power needs

Tackling Choke Point Induced Performance Bottlenecks in a Near-Threshold GPGPU

Over the last decade, General Purpose Graphics Processing Units (GPGPUs) have garnered a substantial attention in the research community due to their extensive thread-level parallelism. GPGPUs provide a remarkable performance improvement over Central Processing Units (CPUs), for highly parallel applications. However, GPGPUs typically achieve this extensive thread-level parallelism at the cost of a large power consumption. Consequently, Near-Threshold Computing (NTC) provides a promising opportunity for designing energy-efficient GPGPUs (NTC-GPUs). However, NTC-GPUs suffer from a crucial Process Variation (PV)-inflicted performance bottleneck, which is called Choke Point. Choke Point is defined as one or small group of gates which is affected by PV. Choke Point is capable of varying the path-delay of circuit and causing different forms of timing violation. In this work, a cross-layer design technique is proposed to tackle the performance impediments caused by choke points in NTC-GPUs

Predicting Critical Warps in Near-Threshold GPGPU Applications Using a Dynamic Choke Point Analysis

General purpose graphics processing units (GP-GPU), owing to their enormous thread-level parallelism, can significantly improve the power consumption at the near-threshold (NTC) operating region, while offering close to a super-threshold performance. However, process variation (PV) can drastically reduce the GPU performance at NTC. In this work, choke points—a unique device-level characteristic of PV at NTC—that can exacerbate the warp criticality problem in GPUs have been explored. It is shown that the modern warp schedulers cannot tackle the choke point induced critical warps in an NTC GPU. Additionally, Choke Point Aware Warp Speculator, a circuit-architectural solution is proposed to dynamically predict the critical warps in GPUs, and accelerate them in their respective execution units. The best scheme achieves an average improvement of ∼39% in performance, and ∼31% in energy-efficiency, over one state-of-the-art warp scheduler, across 15 GPGPU applications, while incurring marginal hardware overheads

Reclaiming Fault Resilience and Energy Efficiency With Enhanced Performance in Low Power Architectures

Rapid developments of the AI domain has revolutionized the computing industry by the introduction of state-of-art AI architectures. This growth is also accompanied by a massive increase in the power consumption. Near-Theshold Computing (NTC) has emerged as a viable solution by offering significant savings in power consumption paving the way for an energy efficient design paradigm. However, these benefits are accompanied by a deterioration in performance due to the severe process variation and slower transistor switching at Near-Threshold operation. These problems severely restrict the usage of Near-Threshold operation in commercial applications. In this work, a novel AI architecture, Tensor Processing Unit, operating at NTC is thoroughly investigated to tackle the issues hindering system performance. Research problems are demonstrated in a scientific manner and unique opportunities are explored to propose novel design methodologies

Toward Reliable, Secure, and Energy-Efficient Multi-Core System Design

Computer hardware researchers have perennially focussed on improving the performance of computers while stipulating the energy consumption under a strict budget. While several innovations over the years have led to high performance and energy efficient computers, more challenges have also emerged as a fallout. For example, smaller transistor devices in modern multi-core systems are afflicted with several reliability and security concerns, which were inconceivable even a decade ago. Tackling these bottlenecks happens to negatively impact the power and performance of the computers. This dissertation explores novel techniques to gracefully solve some of the pressing challenges of the modern computer design. Specifically, the proposed techniques improve the reliability of on-chip communication fabric under a high power supply noise, increase the energy-efficiency of low-power graphics processing units, and demonstrate an unprecedented security loophole of the low-power computing paradigm through rigorous hardware-based experiments

The Whitworthian 2005-2006

The Whitworthian student newspaper, September 2005-May 2006.https://digitalcommons.whitworth.edu/whitworthian/1090/thumbnail.jp