Nicola Kingswell
Ariel Garton
Trupen Modi
Garrett Marshall
I have nearly 20 years of experience working on the growth side of the fitness industry. My experience has included developing strategy and innovation, executive leadership, business development, strategic partnerships, and driving technology.
I've been privileged to contribute in several successful businesses resulting in two IPO's, four Inc. 1000 FGC awards, IHRSA's Rising Star award, and $5m in ARR in under three years in each of my last three endeavors.
Tracy Wan
Lance Dietz
Manu Nair
Manu started his career at Analog Devices India as a mixed-signal designer. From 2015, he has been working on designing neural network accelerators, first at the University of Manchester, followed by a PhD in the topic from the Institute of Neuroinformatics, UZH-ETH Zurich. He co-founded Synthara in 2019 and has since been leading its business development as its CEO. Synthara is focussed on delivering server-class, rapidly-customizable AI accelerators for the next-generation of edge inference applications. The breakthrough performance and low latency delivered by our Adaptiva IP product family enables a whole new class of wearable, smart sensing, and biomedical applications. Synthara is headquartered in Zug, Switzerland and has won a number of awards from various organizations such as Intel Ignite, European Space Agency, Innosuisse, and others.
Testimonials
I have a stake in the game and would be curious to hear directly about the challenges in operating in this industry from my peers. I am also happy to share my experience in running SYnthara and the challenges and opportunities as we see them
Manu Nair
Chris Kachris
Chris Kachris is the founder and CEO of InAccel that helps companies speedup their applications using hardware accelerators in the cloud easier than ever. He holds a Ph.D. in Computer Engineering from Delft University of Technology. He is the editor of the book Hardware Accelerators in Data Centers and he has more than 20 years of experience on FPGAs and hardware accelerators for machine learning, network processing and data processing. His has co-authored more than 80 scientific paper on hardware accelerators and his work has been cited in more than 2000 publications.
Testimonial
There are a lot of companies that now provides powerful AI platforms. However, the main challenge for the widespread adoption of specialized AI chips is the easy of integration with the current software stack and the frameworks that are widely used by ML engineers, data scientist and DevOps. Therefore, there is the need of an abstractions layer (similar to an Operating system in the CPU world) that will allow software developers to fully utilize the power of AI chips without having to change the software. Therefore, it is crucial to discuss the need for the abstraction layers (middleware, runtime system, resource manager) that will allow the AI chips to be easily integration with the software frameworks and the DevOps tools (kybernetes, etc.). The main contribution that will bring into the panel is the need for the software stack for easy deployment, scaling and resource management of the AI chips to be easily adopted by the software and ML community.