A Design Methodology for Robust, Energy-efficient, Application-aware Memory Systems

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Release : 2012
Genre : Computer storage devices
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A Design Methodology for Robust, Energy-efficient, Application-aware Memory Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Design Methodology for Robust, Energy-efficient, Application-aware Memory Systems write by Subho Chatterjee. This book was released on 2012. A Design Methodology for Robust, Energy-efficient, Application-aware Memory Systems available in PDF, EPUB and Kindle. Memory design is a crucial component of VLSI system design from area, power and performance perspectives. To meet the increasingly challenging system specifications, architecture, circuit and device level innovations are required for existing memory technologies. Emerging memory solutions are widely explored to cater to strict budgets. This thesis presents design methodologies for custom memory design with the objective of power-performance benefits across specific applications. Taking example of STTRAM (spin transfer torque random access memory) as an emerging memory candidate, the design space is explored to find optimal energy design solution. A thorough thermal reliability study is performed to estimate detection reliability challenges and circuit solutions are proposed to ensure reliable operation. Adoption of the application-specific optimal energy solution is shown to yield considerable energy benefits in a read-heavy application called MBC (memory based computing). Circuit level customizations are studied for the volatile SRAM (static random access memory) memory, which will provide improved energy-delay product (EDP) for the same MBC application. Memory design has to be aware of upcoming challenges from not only the application nature but also from the packaging front. Taking 3D die-folding as an example, SRAM performance shift under die-folding is illustrated. Overall the thesis demonstrates how knowledge of the system and packaging can help in achieving power efficient and high performance memory design.

Computing with Memory for Energy-Efficient Robust Systems

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Release : 2013-09-07
Genre : Technology & Engineering
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Book Rating : 980/5 ( reviews)

Computing with Memory for Energy-Efficient Robust Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Computing with Memory for Energy-Efficient Robust Systems write by Somnath Paul. This book was released on 2013-09-07. Computing with Memory for Energy-Efficient Robust Systems available in PDF, EPUB and Kindle. This book analyzes energy and reliability as major challenges faced by designers of computing frameworks in the nanometer technology regime. The authors describe the existing solutions to address these challenges and then reveal a new reconfigurable computing platform, which leverages high-density nanoscale memory for both data storage and computation to maximize the energy-efficiency and reliability. The energy and reliability benefits of this new paradigm are illustrated and the design challenges are discussed. Various hardware and software aspects of this exciting computing paradigm are described, particularly with respect to hardware-software co-designed frameworks, where the hardware unit can be reconfigured to mimic diverse application behavior. Finally, the energy-efficiency of the paradigm described is compared with other, well-known reconfigurable computing platforms.

Content-aware Memory Systems for High-performance, Energy-efficient Data Movement

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Release : 2017
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Content-aware Memory Systems for High-performance, Energy-efficient Data Movement - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Content-aware Memory Systems for High-performance, Energy-efficient Data Movement write by Shibo Wang. This book was released on 2017. Content-aware Memory Systems for High-performance, Energy-efficient Data Movement available in PDF, EPUB and Kindle. "Power dissipation and limited memory bandwidth are significant bottlenecks in virtually all computer systems, from datacenters to mobile devices. The memory subsystem is responsible for a significant and growing fraction of the total system energy due to data movement throughout the memory hierarchy. These energy and performance problems become more severe as emerging data-intensive applications place a larger fraction of the data in memory, and require substantial data processing and transmission capabilities. As a result, it is critical to architect novel, energy- and bandwidth-efficient memory systems and data access mechanisms for future computer systems. Existing memory systems are largely oblivious to the contents of the transferred or stored data. However, the transmission and storage costs of data with different contents often differ, which creates new possibilities to reduce the attendant data movement overheads. This dissertation investigates both content aware transmission and storage mechanisms in conventional DRAM systems, such as DDRx, and emerging memory architectures, such as Hybrid Memory Cube (HMC). Content aware architectural techniques are developed to improve the performance and energy efficiency of the memory hierarchy. The dissertation first presents a new energy-efficient data encoding mechanism based on online data clustering that exploits asymmetric data movement costs. One promising way of reducing the data movement energy is to design the interconnect such that the transmission of 0s is considerably cheaper than that of 1s. Given such an interconnect with asymmetric transmission costs, data movement energy can be reduced by encoding the transmitted data such that the number of 1s in each transmitted codeword is minimized. In the proposed coding scheme, the transmitted data blocks are dynamically grouped into clusters based on the similarities between their binary representations. Each cluster has a center with a bit pattern close to those of the data blocks that belong to that cluster. Each transmitted data block is expressed as the bitwise XOR between the nearest cluster center and a sparse residual with a small number of 1s. The data movement energy is minimized by sending the sparse residual along with an identifier that specifies which cluster center to use in decoding the transmitted data. At runtime, the proposed approach continually updates the cluster centers based on the observed data to adapt to phase changes. By dynamically learning and adjusting the cluster centers, the Hamming distance between each data block and the nearest cluster center can be significantly reduced. As a result, the total number of 1s in the transmitted residual is lowered, leading to substantial savings in data movement energy. The dissertation then introduces content aware refresh - a novel DRAM refresh method that reduces the refresh rate by exploiting the unidirectional nature of DRAM retention errors: assuming that a logical 1 and 0 respectively are represented by the presence and absence of charge, 1-to-0 failures dominate the retention errors. As a result, in a DRAM system that uses a block error correcting code (ECC) to protect memory from errors, blocks with fewer 1s exhibit a lower probability of encountering an uncorrectable error. Such blocks can attain a specified reliability target with a refresh rate lower than what is required for a block with all 1s. Leveraging this key insight, and without compromising memory reliability, the proposed content aware refresh mechanism refreshes memory blocks with fewer 1s less frequently. In the proposed content-aware refresh mechanism, the refresh rate of a refresh group - a group of DRAM rows refreshed together?is decided based on the worst case ECC block in that group, which is the block with the greatest number of 1s. In order to keep the overhead of tracking multiple refresh rates manageable, multiple refresh groups are dynamically arranged into one of a predefined number of refresh bins and refreshed at the same rate. To reduce the number of refresh operations, both the refresh rates of the bins and the refresh group-to-bin assignments are adaptively changed at runtime. By tailoring the refresh rate to the actual content of a memory block rather than assuming a worst case data pattern, the proposed content aware refresh technique effectively avoids unnecessary refresh operations and significantly improves the performance and energy efficiency of DRAM systems. Finally, the dissertation examines a novel HMC power management solution that enables energy-efficient HMC systems with erasure codes. The key idea is to encode multiple blocks of data in a single coding block that is distributed among all of the HMC modules in the system, and to store the resulting check bits in a dedicated, always-on HMC. The inaccessible data that are stored in a sleeping HMC module can be reconstructed by decoding a subset of the remaining memory blocks retrieved from other active HMCs, rather than waiting for the sleeping HMC module to become active. A novel data selection policy is used to decide which data to encode at runtime, significantly increasing the probability of reconstructing otherwise inaccessible data. The coding procedure is optimized by leveraging the near memory computing capability of the HMC logic layer. This approach makes it possible to tolerate the latency penalty incurred when switching an HMC between active and sleep modes, thereby enabling a power-capped HMC system."--Pages xi-xiv.

Energy Efficiency and Robustness of Advanced Machine Learning Architectures

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Release : 2024-11-14
Genre : Computers
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Book Rating : 036/5 ( reviews)

Energy Efficiency and Robustness of Advanced Machine Learning Architectures - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Energy Efficiency and Robustness of Advanced Machine Learning Architectures write by Alberto Marchisio. This book was released on 2024-11-14. Energy Efficiency and Robustness of Advanced Machine Learning Architectures available in PDF, EPUB and Kindle. Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals. This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems. This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.

Computing with Memory for Energy-Efficient Robust Systems

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Release : 2011
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Computing with Memory for Energy-Efficient Robust Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Computing with Memory for Energy-Efficient Robust Systems write by Somnath Paul. This book was released on 2011. Computing with Memory for Energy-Efficient Robust Systems available in PDF, EPUB and Kindle. Reconfigurable computing platforms that offer the flexibility to configure hardware resources according to application requirements, provide great opportunity to accelerate wide variety of applications. Over the past decade, Field Programmable Gate Arrays (FPGAs) have grown to be the most popular hardware reconfigurable computing platform. Modern FPGAs integrate an array of spatially distributed logic/memory blocks and programmable routing resources. Such a framework can provide several orders of magnitude more throughput compared to conventional microprocessor based designs. The power and performance of conventional FPGA platform is largely dominated by programmable interconnects, which have poor technological scalability. Moreover, the performance improvement for applications mapped to the FPGA platform is largely limited by the off-chip bandwidth. A reconfigurable framework which minimizes the contribution from the programmable interconnects and mitigates the bandwidth bottleneck by moving the computing engine close to the data is expected to significantly improve the energy efficiency of reconfigurable systems. In this work, we propose a novel hardware reconfigurable framework, referred to as memory based computing (MBC) framework. The main computing fabric for such a framework is a 2-D memory array which is used to store the functional behavior for the mapped application. Each computing element in the framework is temporal in nature and an array of these elements is used to map an application in a spatio-temporal fashion. Temporal execution inside each compute element reduces the requirement for programmable interconnects, thus improving the energy-efficiency over a fully spatial reconfigurable framework. In addition to storing the functional behavior, the memory arrays also store data, thus mitigating the off-chip bandwidth bottleneck. The framework is particularly appealing for system design with many emerging non-silicon nano-devices, which are amenable to dense, regular nonvolatile memory design. With the primary computing fabric being memory, the proposed MBC framework can be made robust to high device failure rates at nanoscale technologies. We have developed architecture and circuit level optimization techniques for the proposed framework along with efficient algorithms for automatic mapping of applications to this framework. Finally, we have investigated application of this framework as a reconfigurable computing resource in a processor for reliability improvement and hardware acceleration.