Collaborating Authors


RISC-V International Ratifies 15 New Specifications, Opening Up New Possibilities for RISC-V Designs - RISC-V International


ZURICH – Dec. 2, 2021 – RISC-V International, a global open hardware standards organization, today announced that RISC-V members have ratified 15 new specifications – representing more than 40 extensions – for the free and open RISC-V instruction set architecture (ISA). Most notably, RISC-V members ratified the Vector, Scalar Cryptography, and Hypervisor specifications which will help unlock new opportunities for developers creating RISC-V applications for artificial intelligence (AI) and machine learning (ML), the Internet of Things (IoT), connected and autonomous cars, data centers, and beyond. "In 2021, RISC-V International made huge leaps in our technical progress as we ratified 15 specifications that are critical for the future of computing," said Krste Asanović, Chair of the RISC-V International Board of Directors. "The development of these specifications really showcased the incredible benefits of open collaboration across companies and geographies as members worked together to develop novel approaches for the latest computing requirements." The RISC-V Vector specification will help accelerate the computation of data intensive operations like ML inference for audio, vision, and voice processing.

Semantic Understanding of Foggy Scenes with Purely Synthetic Data Artificial Intelligence

-- This work addresses the problem of semantic scene understanding under foggy road conditions. Although marked progress has been made in semantic scene understanding over the recent years, it is mainly concentrated on clear weather outdoor scenes. Extending semantic segmentation methods to adverse weather conditions like fog is crucially important for outdoor applications such as self-driving cars. In this paper, we propose a novel method, which uses purely synthetic data to improve the performance on unseen real-world foggy scenes captured in the streets of Zurich and its surroundings. Our results highlight the potential and power of photo-realistic synthetic images for training and especially fine-tuning deep neural nets. Our contributions are threefold, 1) we created a purely synthetic, high-quality foggy dataset of 25,000 unique outdoor scenes, that we call Foggy Synscapes and plan to release publicly 2) we show that with this data we outperform previous approaches on real-world foggy test data 3) we show that a combination of our data and previously used data can even further improve the performance on real-world foggy data. The last years have seen tremendous progress in tasks relevant to autonomous driving [1].