The demand for deploying machine learning models, especially state-of-the-art deep neural networks on edge devices is rapidly growing. Edge AI allows to run inference locally, without the need for a connection to the cloud, which makes the technologies more portable and self-sufficient. Without the need for sending the data to the cloud, edge AI solutions are also much safer in terms of data privacy.| antmicro.com
Co-simulating HDL has been possible in Renode since the 1.7.1 release, but the functionality - critical for hardware/software co-development as well as FPGA use cases - is constantly evolving based on the needs of our customers like Google and Microchip as well as our work in open source groups including CHIPS Alliance and RISC-V International. To quickly recap, by co-simulation we mean a scenario where a part of the system is simulated in Renode but some specific peripheral or subsystem is s...| antmicro.com
We are happy to announce our involvement in ‘Very Efficient Deep Learning in IoT’ (VEDLIoT) - a project funded by the European Commission and coordinated by Bielefeld University’s CoR-Lab, launched at the end of 2020. Comprising a 12-member international research group, VEDLIoT aims to develop a next-generation software/hardware platform for the Internet of Things. Antmicro’s contributions, among other things, will be to leverage its leading position in RISC-V, machine learning and si...| antmicro.com