Jul 15, 2020 · This work presents an extension to the PULP-NN library targeting the acceleration of mixed-precision Deep Neural Networks.

May 23, 2020 · This work presents an extension to the PULP-NN library targeting the acceleration of mixed-precision Deep Neural Networks.

This work presents an extension to the PULP-NN library targeting the acceleration of mixed-precision Deep Neural Networks.

Jul 15, 2020 · This work presents an extension to the PULP-NN library targeting the acceleration of mixed-precision Deep Neural Networks, an emerging paradigm ...

May 13, 2020 · This work presents an extension to the PULP-NN library target- ing the acceleration of mixed-precision Deep Neural Networks, an emerging ...

Our approach enables full support for mixed-precision QNN inference with 292 different combinations of operands at 16-, 8-, 4- and 2-bit precision, without ...

Mixed-precision quantization, where more sensitive layers are kept at higher precision, can achieve the trade-off between accuracy and complexity of neural ...

ABSTRACT. This paper presents a novel end-to-end methodology for enabling the deployment of high-accuracy deep networks on microcontrollers.

The deployment of Quantized Neural Networks (QNN) on advanced microcontrollers requires optimized software to exploit digital signal processing (DSP) extensions ...

Low bit-width Quantized Neural Networks (QNNs) enable deployment of complex machine learning models on constrained devices such as microcontrollers (MCUs) ...