Rangharajan Venkatesan

Learn More
Domain Wall Memory (DWM) is a recently developed spin-based memory technology in which several bits of data are densely packed into the domains of a ferromagnetic wire. DWM has shown great promise in enabling non-volatile memory with unprecedented density and high energy efficiency. In this work, we propose TapeCache, a first attempt to employ DWMs as(More)
Spin-based memories are promising candidates for future on-chip memories due to their high density, non-volatility, and very low leakage. However, the high energy and latency of write operations in these memories is a major challenge. In this work, we explore a new approach -- shift based write -- that offers a fast and energy-efficient alternative to(More)
General-purpose Graphics Processing Units (GPGPUs) are widely used for executing massively parallel workloads from various application domains. Feeding data to the hundreds to thousands of cores that current GPGPUs integrate places great demands on the memory hierarchy, fueling an ever-increasing demand for on-chip memory. In this work, we propose STAG, a(More)
Spintronic memories offer superior density, non-volatility and ultra-low standby power compared to CMOS memories, and have consequently attracted great interest in the design of on-chip caches. Domain Wall Memory (DWM) is a spintronic memory technology with unparalleled density arising from a tape-like structure. However, such a structure involves(More)
Spintronic memories are considered to be promising candidates for future on-chip memories due to their high density, nonvolatility, and near-zero leakage. However, they also face challenges such as high write energy and latency and limited read speed due to single-ended sensing. Further, the conflicting requirements of read and write operations lead to(More)
Approximate computing, which refers to a class of techniques that relax the requirement of exact equivalence between the specification and implementation of a computing system, has attracted significant interest in recent years. We propose a systematic methodology, called MACACO, for the <u>M</u>odeling and <u>A</u>nalysis of(More)
Deep Learning Networks (DLNs) are bio-inspired large-scale neural networks that are widely used in emerging vision, analytics, and search applications. The high computation and storage requirements of DLNs have led to the exploration of various avenues for their efficient realization. Concurrently, the ability of emerging post-CMOS devices to efficiently(More)
Spin-based devices promise to revolutionize computing platforms by enabling high-density, low-leakage memories. However, stringent tradeoffs between critical design metrics such as read and write stability, reliability, density, performance and energy-efficiency limit the efficiency of conventional spin-transfer- torque devices and bit-cells. We propose a(More)
Emerging workloads such as Recognition, Mining and Synthesis present great opportunities for many-core parallel computing, but also place significant demands on the memory system. Spin-based devices have shown great promise in enabling high-density, energy-efficient memory. In this paper, we present the design and evaluation of a many-core domain-specific(More)