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Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and…
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CAS-CNN: A deep convolutional neural network for image compression artifact suppression
TLDR
This work presents a novel 12-layer deep convolutional network for image compression artifact suppression with hierarchical skip connections and a multi-scale loss function and shows that a network trained for a specific quality factor is resilient to the QF used to compress the input image.
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Origami: A Convolutional Network Accelerator
TLDR
This paper presents the first convolutional network accelerator which is scalable to network sizes that are currently only handled by workstation GPUs, but remains within the power envelope of embedded systems.
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YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights
TLDR
A HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s on a core area of only 1.33 MGE and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V is presented.
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YodaNN: An Architecture for Ultralow Power Binary-Weight CNN Acceleration
TLDR
This paper presents an accelerator optimized for binary-weight CNNs that significantly outperforms the state-of-the-art in terms of energy and area efficiency and removes the need for expensive multiplications, as well as reducing I/O bandwidth and storage.
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Origami: A 803-GOp/s/W Convolutional Network Accelerator
TLDR
A new architecture, design, and implementation, as well as the first reported silicon measurements of such an accelerator, outperforming previous work in terms of power, area, and I/O efficiency are presented.
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Accelerating real-time embedded scene labeling with convolutional networks
TLDR
This paper presents an optimized convolutional network implementation suitable for real-time scene labeling on embedded platforms and demonstrates that for scene labeling this approach achieves a 1.5x improvement in throughput when compared to a modern desktop CPU at a power budget of only 11 W.
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CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams
TLDR
This work adopts an orthogonal viewpoint and proposes a novel algorithm exploiting the spatio-temporal sparsity of pixel changes that resulted in an average speed-up of 9.1X over cuDNN on the Tegra X2 platform at a negligible accuracy loss and a lower power consumption.
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Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms
TLDR
Laelaps is an energy-efficient and fast learning algorithm with no false alarms for epileptic seizure detection from long-term intracranial electroencephalography (iEEG) signals that surpasses those yielded by state-of-the-art methods on a new very large dataset containing 116 seizures of 18 drug-resistant epilepsy patients.
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EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain–Machine Interfaces
TLDR
This paper proposes EEG-TCNet, a novel temporal convolutional network (TCN) that achieves outstanding accuracy while requiring few trainable parameters, which makes it suitable for embedded classification on resource-limited devices at the edge.
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