A 3 TOPS/W RISC-V Parallel Cluster for Inference of Fine-Grain Mixed-Precision Quantized Neural Networks
Author:
Affiliation:
1. University of Bologna,Department of Electrical, Electronic and Information Engineering (DEI),Italy
2. IIS Integrated Systems Laboratory, ETH Zurich,Switzerland
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10238464/10238482/10238679.pdf?arnumber=10238679
Reference18 articles.
1. Enabling mixed-precision quantized neural networks in extreme-edge devices
2. CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices
3. A Mixed-Precision RISC-V Processor for Extreme-Edge DNN Inference
4. XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V Based IoT End Nodes
5. Near-Threshold RISC-V Core With DSP Extensions for Scalable IoT Endpoint Devices
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