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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. Expand
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. Expand
InfiniTime: Multi-sensor wearable bracelet with human body harvesting
TLDR
Simulations using energy intake measurements from solar and TEG modules confirm that InfiniTime achieves self-sustainability with indoor lighting levels and body heat for several realistic applications featuring data acquisition from the on-board camera and multiple sensors, as well as visualization and wireless connectivity. Expand
XNORBIN: A 95 TOp/s/W hardware accelerator for binary convolutional neural networks
TLDR
XNORBIN is presented, a flexible accelerator for binary CNNs with computation tightly coupled to memory for aggressive data reuse supporting even non-trivial network topologies with large feature map volumes. Expand
Accelerated Visual Context Classification on a Low-Power Smartwatch
TLDR
The results suggest that the proposed heterogeneous platform can provide up to 500× speedup with respect to the MSP430 within a similar power envelope, which would enable complex computer vision algorithms to be executed in highly power-constrained scenarios. Expand
Stretchable and Conformable Oxide Thin-Film Electronics
Stretchable large-area high-performance amorphous oxide thin-film electronics fabricated using locally reinforced composite elastomers or wavy structures are functional while elongated by >200% andExpand
SIR10US: A tightly coupled elliptic-curve cryptography co-processor for the OpenRISC
TLDR
The authors present an application-specific co-processor for digital signature verification according to the Elliptic Curve Digital Signature Algorithm (ECDSA) based on the NIST B-233 standard. Expand
Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine
TLDR
Hyperdrive is presented: a BWN accelerator dramatically reducing the I/O bandwidth exploiting a novel binary-weight streaming approach, which can be used for an arbitrarily sized convolutional neural network architecture and input resolution by exploiting the natural scalability of the compute units both at chip-level and system-level. Expand
Hyperdrive: A Systolically Scalable Binary-Weight CNN Inference Engine for mW IoT End-Nodes
TLDR
Hyperdrive is presented: a BWN accelerator dramatically reducing the I/O bandwidth exploiting a novel binary-weight streaming approach, and capable of handling high-resolution images by virtue of its systolic-scalable architecture. Expand
ChewBaccaNN: A Flexible 223 TOPS/W BNN Accelerator
TLDR
ChewBaccaNN is presented, a 0.7 mm2 sized binary convolutional neural network (CNN) accelerator designed in GlobalFoundries 22 nm technology that can perform CIFAR-10 inference at 86.8% accuracy and perform inference on a binarized ResNet-18 trained with 8-bases Group-Net to achieve a 67.5% Top-1 accuracy. Expand
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