An Efficient Analog Convolutional Neural Network Hardware Accelerator Enabled by a Novel Memoryless Architecture for Insect-Sized Robots
Author:
Affiliation:
1. University of Tartu,Intelligent Materials and Systems Laboratory (IMS Laboratory) Institute of Technology,Tartu,Eatonia
2. Tallinn University of Technology,Centre for Dependable Computing Systems,Department of Computer Systems,Tallinn,Eatonia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9837335/9837476/09837551.pdf?arnumber=9837551
Reference27 articles.
1. Four Wings: An Insect-Sized Aerial Robot With Steering Ability and Payload Capacity for Autonomy
2. Modeling and Experimental Analysis of the Mass Loading Effect on Micro-Ionic Polymer Actuators Using Step Response Identification
3. A survey of FPGA-based accelerators for convolutional neural networks
4. Always-On 674μ W@4GOP/s Error Resilient Binary Neural Networks With Aggressive SRAM Voltage Scaling on a 22-nm IoT End-Node
5. Design, fabrication and initial results of a 2g autonomous glider
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