With every passing year, it becomes clearer that AI is the technology to dominate the coming decades. AI is projected to be the foundation for a $100B industry, but for that to happen, it must overcome the challenges inherent to its nature.
The demand for speed, processing power and efficiency is ever increasing – faster than it can be supplied by conventional means.
CogniFiber will change that.
With our DeepLight.
The AI hardware technology that will change the world of computing forever.
By applying cutting edge research and technology from the fields of photonics, AI and advanced electronics, we can deliver devices that are 10.000x faster, consume only 1% of the power and need no buffering at all.
This symbolizes the next step in the evolution of information technology.
DeepLight will solve speed, power consumption and buffering problems.
This makes scalable photonic computing the emerging technology of the coming decades – allowing AI to be cheaper, increasingly mobile and more accessible in the process.
Aside from their performance superiority over existing technology, DeepLight devices have been carefully engineered to come with multiple added benefits:
This and more make DeepLight the perfect solution for the growing need of the global information economy.
The concept behind DeepLight Technology is converting a flaw into a feature. When using fiber optics that transmit data by means of light, precautions need to be made to prevent light from crossing between fibers and producing errors and noise.
DeepLight lets this cross-talk between fibers happen in a controlled way. This way cross-talk is being transformed from noise to computation. This is possible through our unique design and the use of smart algorithms that allow us to program, how the fibers interact and influence one another. What this does is essentially convert strands of fiber into programmable nodes that can be used as neurons in a neural network, enabling the training r of sophisticated AI systems and their light speed running after training.
CogniFiber pioneers this one of a kind technology of the future. Nobody else has yet managed to make any progress on this bleeding edge of research. Not only will DeepLight revolutionize the world of computing but it’ll also make AI cheaper, more mobile and more accessible to the broader markets. Its hidden power lies in the scalability, that’s simply not possible with other technologies.
Thanks to years of research and ceaseless striving for perfection, our engineers have reached new heights. We are proud to present to you some of the specs our DeepLight Technology comes with.
Current models are trained within the server. Future deployments will include automated conversions from other neural network architectures such as TensorFlow and ONNX etc.
Our next-generation product is a larger scale deployment which we call an optical data center.
Dr. Cohen has been envisioning and implementing photonics-based solutions for various uses since 2012. He is an experienced hardware and photonics architect, a machine learning aficionado, and a neurobiology lecturer. He completed his B.Sc. In Electronics engineering and Biology (Technion), M.Sc. and Ph.D. in neurobiology (Weizmann Institute) and his post-doc in optical neuromorphics (Bar Ilan and Hebrew Universities). Eyal has previously held positions of algorithm team leader (M.S. Tech) and hardware engineer (Mellanox Semiconductor, Saifun Semiconductor).
Together with Dr. Cohen in 2012, Prof. Zavlevsky began the promising research project that ended up showing that neural networks can be implemented in-fiber and thus enable light speed inferencing. Prof. Zalevsky is a professor and Dean of the Faculty of Engineering at Bar Ilan University. His research expertise involves biomedical optics, super-resolution, and electro-optical devices. He has published more than 450 peer-reviewed papers, 10 books, 30 book chapters, and around 100 patents. For his work and great service to science, he received many national and international scientific prizes. He also already is a co-founder and technological leader in many successful startup companies that originated from technologies commercialized from his lab.
Ms. Levy joined Dr. Cohen and Prof. Zalevsky in 2017 as a co-founder and brought the business strategy and operations experience needed to lead the financing round, launch the company and start strategic partnerships. Ms. Levy has accumulated over 12 years of experience in strategy and operations management and held consulting and executive positions in both startups and enterprises (Treeway, Sheernetworks, Hotbar and MentorWave) – three of which she successfully exited. She has a B.Sc. in Software Engineering from Tel Aviv University and an MBA from New York Polytechnic.
Additionally to our three founders, CogniFiber relies on an extraordinary team of experts across a wide variety of fields. Optics, AI and advanced electronics are just the tip of the iceberg. Developing and creating DeepLight Technology is a multidisciplinary effort, that wouldn’t be possible without the great and diverse team at CogniFiber.
Israeli startup CogniFiber strives to reinvent the chip
The company, which has won through to the 4YFN startup competition final at the Mobile World Congress in Barcleona, is planning mega-fast processors.
COGNIFIBER HAS BEEN SELECTED TO TAKE PART IN INTEL’S INGENUITY PARTNER PROGRAM (INTEL IPP) As part of the program, CogniFiber will be mentored by experts from Intel’s business organizations, will be exposed to state-of-the-art technology, along with an educational infrastructure and local and international events.
Cognifiber: Make AI cheaper, more mobile and more accessible with In-fiber photonic computing devices.
4YFN began as a complimentary event to Mobile World Congress with the aim of connecting startups with corporations and investors. In it’s 5th edition, and with more than 20,000 attendees, we can now say it is no longer a side event.
4YFN Awards to Identify Best International Startup
Cognifiber (Israel): a world-leading company in the field of scalable photonic computing.