Xpulpnn: Enabling energy efficient and flexible inference of quantized neural networks on risc-v based iot end nodes
Heavily quantized fixed-point arithmetic is becoming a common approach to deploy
Convolutional Neural Networks (CNNs) on limited-memory low-power IoT end-nodes …
Convolutional Neural Networks (CNNs) on limited-memory low-power IoT end-nodes …
A mixed-precision RISC-V processor for extreme-edge DNN inference
Low bit-width Quantized Neural Networks (QNNs) enable deployment of complex machine
learning models on constrained devices such as microcontrollers (MCUs) by reducing their …
learning models on constrained devices such as microcontrollers (MCUs) by reducing their …
Flexible Computing Systems for AI Acceleration at the Extreme Edge of the IoT
A Garofalo - 2022 - amsdottorato.unibo.it
Embedding intelligence in extreme edge devices allows distilling raw data acquired from
sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in …
sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in …