4 research outputs found

    A Modern Primer on Processing in Memory

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    Modern computing systems are overwhelmingly designed to move data to computation. This design choice goes directly against at least three key trends in computing that cause performance, scalability and energy bottlenecks: (1) data access is a key bottleneck as many important applications are increasingly data-intensive, and memory bandwidth and energy do not scale well, (2) energy consumption is a key limiter in almost all computing platforms, especially server and mobile systems, (3) data movement, especially off-chip to on-chip, is very expensive in terms of bandwidth, energy and latency, much more so than computation. These trends are especially severely-felt in the data-intensive server and energy-constrained mobile systems of today. At the same time, conventional memory technology is facing many technology scaling challenges in terms of reliability, energy, and performance. As a result, memory system architects are open to organizing memory in different ways and making it more intelligent, at the expense of higher cost. The emergence of 3D-stacked memory plus logic, the adoption of error correcting codes inside the latest DRAM chips, proliferation of different main memory standards and chips, specialized for different purposes (e.g., graphics, low-power, high bandwidth, low latency), and the necessity of designing new solutions to serious reliability and security issues, such as the RowHammer phenomenon, are an evidence of this trend. This chapter discusses recent research that aims to practically enable computation close to data, an approach we call processing-in-memory (PIM). PIM places computation mechanisms in or near where the data is stored (i.e., inside the memory chips, in the logic layer of 3D-stacked memory, or in the memory controllers), so that data movement between the computation units and memory is reduced or eliminated.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0398

    A GPGPU-Based Acceleration of Fault-Tolerant MLP Learnings

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    Anales del XIII Congreso Argentino de Ciencias de la Computaci贸n (CACIC)

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    Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterog茅neas Redes de Avanzada Redes inal谩mbricas Redes m贸viles Redes activas Administraci贸n y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad inform谩tica y autenticaci贸n, privacidad Infraestructura para firma digital y certificados digitales An谩lisis y detecci贸n de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integraci贸n (Web Services o .Net)Red de Universidades con Carreras en Inform谩tica (RedUNCI

    Anales del XIII Congreso Argentino de Ciencias de la Computaci贸n (CACIC)

    Get PDF
    Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterog茅neas Redes de Avanzada Redes inal谩mbricas Redes m贸viles Redes activas Administraci贸n y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad inform谩tica y autenticaci贸n, privacidad Infraestructura para firma digital y certificados digitales An谩lisis y detecci贸n de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integraci贸n (Web Services o .Net)Red de Universidades con Carreras en Inform谩tica (RedUNCI
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