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The memory subsystem accounts for a significant cost and power budget of a computer system. Current DRAM-based main memory systems are starting to hit the power and cost limit. An alternative memory technology that uses resistance contrast in phase-change materials is being actively investigated in the circuits community. <i>Phase Change Memory (PCM)</i>(More)
Phase Change Memory (PCM) is an emerging memory technology that can increase main memory capacity in a cost-effective and power-efficient manner. However, PCM cells can endure only a maximum of 10<sup>7</sup> - 10<sup>8</sup> writes, making a PCM based system have a lifetime of only a few years under ideal conditions. Furthermore, we show that(More)
While Processing-in-Memory has been investigated for decades, it has not been embraced commercially. A number of emerging technologies have renewed interest in this topic. In particular, the emergence of 3D stacking and the imminent release of Micron's Hybrid Memory Cube device have made it more practical to move computation near memory. However, the(More)
Leakage power is a major concern in current and future microprocessor designs. In this paper, we explore the potential of architectural techniques to reduce leakage through power-gating of execution units. This paper first develops parameterized analytical equations that estimate the break-even point for application of power-gating techniques. The potential(More)
One of the key scalability challenges of on-chip coherence in a multicore chip is the coherence directory, which provides information on sharing of cache blocks. Shadow tags that duplicate entire private cache tag arrays are widely used to minimize area overhead, but require an energy-intensive associative search to obtain the sharing information. Recent(More)
Technology constraints have increasingly led to the adoption of specialized coprocessors, i.e. hardware accelerators. The first challenge that computer architects encounter is identifying &#x0022;what to specialize in the program&#x0022;. We demonstrate that this requires precise enumeration of program paths based on dynamic program behavior. We hypothesize(More)
Deep Neural Networks (DNNs) have emerged as a powerful and versatile set of techniques showing successes on challenging artificial intelligence (AI) problems. Applications in domains such as image/video processing, autonomous cars, natural language processing, speech synthesis and recognition, genomics and many others have embraced deep learning as the(More)
There exist a multitude of execution models available today for a developer to target. The choices vary from general purpose processors to fixed-function hardware accelerators with a large number of variations in-between. There is a growing demand to assess the potential benefits of porting or rewriting an application to a target architecture in order to(More)
—Increasing demand for power-efficient, high-performance computing has spurred a growing number and diversity of hardware accelerators in mobile and server Systems on Chip (SoCs). This paper makes the case that the co-design of the accelerator microarchitecture with the system in which it belongs is critical to balanced, efficient accelerator(More)
Approximate computing is an emerging paradigm enabling tradeoffs between accuracy and efficiency. However, a fundamental challenge persists: state-of-the-art techniques lack the ability to enforce runtime guarantees on accuracy. The convention is to 1) employ training/rollback mechanisms which add complexity, or 2) present experimental results that(More)