Resolving the Memory Bottleneck for Single Supply Near-Threshold Computing

This paper focuses on a review of state-of-the-art memory designs and new design methods for near-threshold computing (NTC). In particular, it presents new ways to design reliable low-voltage NTC memories cost-effectively by reusing available cell libraries, or by adding a digital wrapper around existing commercially available memories. The approach is based on modeling at system level supported by silicon measurement on a test chip in a 40nm low-power processing technology. Advanced monitoring, control and run-time error mitigation schemes enable the operation of these memories at the same optimal near-Vt voltage level as the digital logic. Reliability degradation is thus overcome and this opens the way to solve the memory bottleneck in NTC systems. Starting from the available 40 nm silicon measurements, the analysis is extended to future 14 and 10 nm technology nodes

-Memory Computing Based Reliable and High Speed Schmitt trigger 10T SRAM cell design

Static random access memories (SRAM) are useful building blocks in various applications, including cache memories, integrated data storage systems, and microprocessors. The von Neumann bottleneck difficulties are solved by in-memory computing. It eliminates unnecessary frequent data transfer between memory and processing units simultaneously. In this research, the replica-based 10T SRAM design for in-memory computing (IMC) is designed by adapting the word line control scheme in 14nm CMOS technology. In order to achieve high reading and writing capability, the Schmitt trigger inverter was used for energy-saving and stable use. To speed up the writing process of the design, a single transistor is inserted between the cross-coupled inverters. In addition, to increase the node capacity, the voltage boosting circuitry is emphasized. The adaptive word line control scheme was utilized by integrating the replica column based circuit. The Replica approach regulates signal flow through the core by using a dummy column and a dummy row in RAM. To demonstrate the viability of the suggested design, the simulated outcomes are contrasted with those of existing designs. The various performance metrics examined are Read Static Noise Margin (RSNM), Write (WSNM), Hold (HSNM), Read Access Delay (RAD), Write Access Delay (WAD), Read performance and Write performance the varying supply voltage is evaluated

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

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

PYDAC: A DISTRIBUTED RUNTIME SYSTEM AND PROGRAMMING MODEL FOR A HETEROGENEOUS MANY-CORE ARCHITECTURE

Heterogeneous many-core architectures that consist of big, fast cores and small, energy-efficient cores are very promising for future high-performance computing (HPC) systems. These architectures offer a good balance between single-threaded perfor- mance and multithreaded throughput. Such systems impose challenges on the design of programming model and runtime system. Specifically, these challenges include (a) how to fully utilize the chip’s performance, (b) how to manage heterogeneous, un- reliable hardware resources, and (c) how to generate and manage a large amount of parallel tasks. This dissertation proposes and evaluates a Python-based programming framework called PyDac. PyDac supports a two-level programming model. At the high level, a programmer creates a very large number of tasks, using the divide-and-conquer strategy. At the low level, tasks are written in imperative programming style. The runtime system seamlessly manages the parallel tasks, system resilience, and inter- task communication with architecture support. PyDac has been implemented on both an field-programmable gate array (FPGA) emulation of an unconventional het- erogeneous architecture and a conventional multicore microprocessor. To evaluate the performance, resilience, and programmability of the proposed system, several micro-benchmarks were developed. We found that (a) the PyDac abstracts away task communication and achieves programmability, (b) the micro-benchmarks are scalable on the hardware prototype, but (predictably) serial operation limits some micro-benchmarks, and (c) the degree of protection versus speed could be varied in redundant threading that is transparent to programmers