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An Accelerator for High Efficient Vision Processing

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An Out-of-Order Load-Store Queue for Spatial Computing

Paolo Ienne, Lana Josipovic, Philip Brisk

The efficiency of spatial computing depends on the ability to achieve maximal parallelism. This necessitates memory interfaces that can correctly handle memory accesses that arrive in arbitrary order while still respecting data dependencies and ensuring ap ...

Assoc Computing Machinery2017

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Remarkable hardware robustness of deep learning (DL) is revealed by error injection analyses performed using a custom hardware model implementing parallelized restricted Boltzmann machines (RBMs). RBMs in deep belief networks demonstrate robustness against ...

Ieee-Inst Electrical Electronics Engineers Inc2017

Automatic Generation of Efficient Accelerators for Reconfigurable Hardware

Christos Kozyrakis, Yunqi Zhang

Acceleration in the form of customized datapaths offer large performance and energy improvements over general purpose processors. Reconfigurable fabrics such as FPGAs are gaining popularity for use in implementing application-specific accelerators, thereby ...

Ieee2016

HRL: Efficient and Flexible Reconfigurable Logic for Near-Data Processing

Christos Kozyrakis

The energy constraints due to the end of Dennard scaling, the popularity of in-memory analytics, and the advances in 3D integration technology have led to renewed interest in near-data processing (NDP) architectures that move processing closer to main memo ...

Ieee2016

Accelerating Machine Learning with Non-Volatile Memory: exploring device and circuit tradeoffs

Alessandro Fumarola, Severin Sidler

Large arrays of the same nonvolatile memories (NVM) being developed for Storage-Class Memory (SCM) such as Phase Change Memory (PCM) and Resistance RAM (ReRAM) - can also be used in non-Von Neumann neuromorphic computational schemes, with device conductanc ...

Ieee2016

Near-Memory Data Services

Babak Falsafi

This column introduces seven invited position papers about the challenges and opportunities in near-data processing. "Near-Memory Data Services" by Babak Falsafi, "Automata Processing: The Memory Is the Processor!" by Mircea Stan, Kevin Skadron, "Overcomin ...

Institute of Electrical and Electronics Engineers2016

Limited resources of embedded devices and increased real time constraints have raised interest to the binary description methods over the floating-point ones such as SIFT and SURF in computer vision applications. Although many software applications of the ...

Ieee2016

A Process Compensated Gain Cell Embedded-DRAM for Ultra-Low-Power Variation-Aware Design

Andreas Peter Burg, Adam Shmuel Teman, Robert Giterman, Pascal Andreas Meinerzhagen

Gain cell embedded DRAM (GC-eDRAM) is a high-density alternative to SRAM for ultra-low-power systems. However, due to its dynamic nature, GC-eDRAM requires power-hungry refresh cycles to ensure data retention. Traditional design approaches dictate configur ...

Ieee2016

ShiDianNao: shifting vision processing closer to the sensor

Paolo Ienne, Robert Fasthuber, Zidong Du

In recent years, neural network accelerators have been shown to achieve both high energy efficiency and high performance for a broad application scope within the important category of recognition and mining applications. Still, both the energy efficiency a ...

ACM2015

Memory Systems and Interconnects for Scale-Out Servers

Stavros Volos

The information revolution of the last decade has been fueled by the digitization of almost all human activities through a wide range of Internet services. The backbone of this information age are scale-out datacenters that need to collect, store, and proc ...

EPFL2015