2017 · 154 citations · 40 references
EngineeringComputer EngineeringComputer ScienceComputer ArchitectureEmbedded SystemsHardware-software Co-designAccelerator ArchitectureDeep LearningEnergy-efficient Embedded SystemsHardware AccelerationLow-power Fpga DesignParallel ArchitecturesAcceleratorsConvolutional Neural NetworksDeep Learning AcceleratorsEnergy-efficient Hardware AccelerationHardware ResourcesPower-efficient Accelerator DesignPower-efficient DesignScalability
With the recent advance of wearable devices and Internet of Things (IoTs), it becomes attractive to implement the Deep Convolutional Neural Networks (DCNNs) in embedded and portable systems. Currently, executing the software-based DCNNs requires high-performance servers, restricting the widespread deployment on embedded and mobile IoT devices. To overcome this obstacle, considerable research efforts have been made to develop highly-parallel and specialized DCNN accelerators using GPGPUs, FPGAs or ASICs.
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Advances in Neural Information Processing Systems 28
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