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Found 5 papers, 2 papers with code
Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices
In this work, we present Eyeriss v2, a DNN accelerator architecture designed for running compact and sparse DNNs.
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning.
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.
Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision
Computer vision enables a wide range of applications in robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics.
Hardware for Machine Learning: Challenges and Opportunities
Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day.
