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An Accelerator for High Efficient Vision Processing

Publications associées (63)

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Architecture Exploration and Optimization of Heterogeneous Many-Core Compute and Memory Architectures with Architectural Extensions

Yasir Mahmood Qureshi

The expeditious proliferation of Internet connectivity and the growing adoption of digital products have transformed various spheres of our everyday lives. This increased digitization of society has led to the emergence of new applications, which are deplo ...

EPFL2021

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Are (feedforward) convolutional neural networks (CNNs) good models for the human visual system? Here, we used visual crowding as a well-controlled psychophysical test to probe CNNs. Visual crowding is a ubiquitous breakdown of object recognition in the hum ...

2021

Equinox: Training (for Free) on a Custom Inference Accelerator

Babak Falsafi, Martin Jaggi, Louis Coulon, Ahmet Caner Yüzügüler, Mario Paulo Drumond Lages De Oliveira, Arash Pourhabibi Zarandi

DNN inference accelerators executing online services exhibit low average loads because of service demand variability, leading to poor resource utilization. Unfortunately, reclaiming idle inference cycles is difficult as other workloads can not execute on a ...

ACM2021

Crowding and the Architecture of the Visual System

Adrien Christophe Doerig

Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural ...

EPFL2020

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Classically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, we pre ...

2020

Replica Bit-Line Technique for Internal Refresh in Logic-Compatible Gain-Cell Embedded DRAM

Robert Giterman, Odem Harel

Embedded memories, mostly implemented with static random access memory (SRAM), dominate the area and power of integrated circuits. Gain-cell embedded DRAM (GC-eDRAM) is an alternative to SRAM due to its high density, low power consumption, and two-ported f ...

ELSEVIER SCI LTD2020

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Neural networks have become indispensable for a wide range of applications, but they suffer from high computational- and memory-requirements, requiring optimizations from the algorithmic description of the network to the hardware implementation. Moreover, ...

IEEE2020

A Product Engine for Energy-Efficient Execution of Binary Neural Networks Using Resistive Memories

David Atienza Alonso, Marina Zapater Sancho, Pierre-Emmanuel Julien Marc Gaillardon, Xifan Tang, Yasir Mahmood Qureshi, Edouard Giacomin, João Miguel Morgado Pereira Vieira

The need for running complex Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNNs), in edge devices, which are highly constrained in terms of computing power and energy, makes it important to execute such applications efficiently. ...

2019

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For efficient acceleration on FPGA, it is essential for external memory to match the throughput of the processing pipelines. However, the usable DRAM bandwidth decreases significantly if the access pattern causes frequent row conflicts. Memory controllers ...

IEEE COMPUTER SOC2019

The effective bandwidth of the FPGA external memory, usually DRAM, is extremely sensitive to the access pattern. Nonblocking caches that handle thousands of outstanding misses (miss-optimized memory systems) can dynamically improve bandwidth utilization wh ...

IEEE2019