Growing with the remarkable development of digital data of images, videos, and speech from sources, for example, online media and the internet-of-things is driving the requirement for analytics to make that information justifiable and noteworthy. Data analytics frequently depend on machine learning (ML) algorithms. Among ML algorithms, deep convolutional neural networks (DNNs) offer cutting edge precision for significant image classification errands and are getting widely adopted. The renewed interest in artificial intelligence in the previous decade has been a boon for the graphics cards industry. Organizations like Nvidia and AMD have seen an immense lift to their stock prices as their GPUs have demonstrated to be effective for training and running deep learning models.
US machine learning compiler expert Mipsology has teamed with Japanese design and development company OKI IDS on FPGA-based high speed image processing AI services.OKI IDS will use Mipsology's Zebra ML inference accelerator for application designs targeting the fast-growing ML needs in the Japanese market: automotive, industrial robotics, smart cities, medical applications, and video monitoring."We are excited by our collaboration with OKI IDS to supply the Japanese demand for neural network computation that supports high-speed, low-latency image processing," said Ludovic Larzul, CEO and founder of Mipsology. "OKI IDS and Mipsology have a natural synergy and mutual expertise in FPGA design and machine learning that will result in our delivering industry-leading products based on FPGAs."ML Zebra-enabled FPGAs can compute massive amounts of ML data with exceptionally high efficiency, 2.5X more than GPUs, and demonstrate lower power consumption, better environmental adaptability, and a significantly longer lifespan than GPUs, greatly reducing total cost of ownership (TCO).OKI IDS's expertise in designing complex FPGA-based systems, now …
In this special guest feature, Ludovic Larzul, Founder and CEO, Mipsology, describes how in the future, AI will be everywhere. And though some computation will continue to take place in data centers, more will happen at the edge. Ludovic Larzul has more than 25 years of experience driving product development, and has authored 16 technical patents. He previously co-founded and served as VP of engineering for Emulation and Verification Engineering (EVE), a startup that designed specialized ASIC validating supercomputers. Ludovic led the company to a 2012 acquisition by Synopsys, where he served as R&D group director before founding Mipsology in 2018.
Global technology solutions provider Avnet Asia and AI software innovator Mipsology announced that Avnet will promote and resell Mipsology's Zebra software platform to its APAC customer base. Zebra removes the technical complexity of FPGAs, making them plug-and-play with fast performance. This agreement extends Avnet's IoT ecosystem, bringing Mipsology's deep learning inference acceleration solution to its Asia customers. Companies looking to deploy AI can now seamlessly migrate to new FPGA-based acceleration technologies with no code change and enjoy a longer lifespan for software and hardware than they could with GPU-based solutions. Avnet's first product incorporating the solution will be the Zebra-powered Xilinx Alveo data center accelerator cards.
The year 2019 saw unprecedented growth in AI research, development and deployment. Great technical progress has been achieved in image recognition, image generation, natural language understanding and other fields; while challenges remain with data management, efficiency measurement, computational capacity and other issues. To welcome 2020 with some fresh AI perspectives, Synced spoke with global researchers from Google Brain, Sony AI, Alibaba affiliate Ant Financial (formerly known as Alipay), Israel-based AI processor company Habana (recently acquired by Intel), Russian tech giant Yandex, Vietnam's newly established research lab VinAI Research, French deep learning inference acceleration startup Mipsology, and China-based remote sensing data platform TerraQuanta. Colin Raffel, Senior Research Scientist, Google Brain In 2019 the community made huge progress on learning from limited labels. MixMatch, UDA, S4L, and ReMixMatch produced huge gains on standard semi-supervised learning benchmarks.