Edge AI Enabler
mightyFP™ Scalable AI Acceleration for the Edge and Beyond. A silicon-proven AI accelerator IP that delivers unmatched performance, energy efficiency, and integration flexibility across defense and commercial applications
mightyFP™ - Our Flagship Product
US-engineered, standard-based, and patent-pending AI accelerator IP core for portability and interoperability.
Precision-scalable IP core: FP32 to FP4 and beyond
IEEE 754 & 3109 compliant, silicon-proven, and DoD compliant (e.g., FAR, DFARS, NDAA)
Single-digit-cycle latency with patent-pending fused operations.
60× faster inference, 6× faster training
90% reduction in energy consumption
87% memory savings
Delivered as synthesizable RTL for integration into ASICs, FPGAs, or SoCs
Awards & Testimonials
Dr. Alexander Chen
Physicist, AFRL
Strong potential to deliver substantial benefits for edge AI applications, ISR workloads, and autonomous systems
Mr. Mark Bernell
Principal Computer Scientist, AFRL
Compelling potential for advancing the Department of the Air Force’s capabilities in energy-efficient, low-latency AI processing at the edge
Enabling Real-World Solutions
Edge AI for Tactical Platforms
mightyFP™ meets the demands of size, weight, power, cost, and cooling (SWaPC2) constrained environments with ultra-low-power operation and compact silicon footprint. Ideal for UAVs, ISR pods, wearables, and autonomous systems
Datacenter AI Infrastructure
mightyFP™ replaces expensive, power-hungry GPUs with a compact, energy-efficient alternative that eliminates nonessential intermediate data generation and the associated memory storage, movement and compute overhead…..
AR/VR/XR Applications
With optimized trigonometric and reciprocal function support, mightyFP™ enhances 3D raycasting, object projection, and environment-aware overlays – enabling immersive AR/VR/XR experiences
Space and Aerospace AI
Radiation-hardened, format-flexible, and trusted-foundry ready, mightyFP™ accelerates sensor fusion and AI-based autonomy in orbital and flight systems
Customer Success Stories
AI-Powered AR Glasses
A wearable manufacturer deployed an early version of mightyFP™ to enable on-device computer vision and contextual overlays, extending battery life while meeting strict bandwidth, latency, and thermal constraints.
Datacenter Inference
A hyperscaler integrated an early version of mightyFP™ to reduce GPU dependency in LLM inference, achieving up to 30× faster throughput and 65% energy savings
Experts behind mightyFP™
Founder & CEO
David Chen
Co-author of the IEEE 754 Standard and contributor to the upcoming IEEE 3109 Standard. Entrepreneur with 30+ years of experience advancing arithmetic research, semiconductor innovation, and IP commercialization, with FPU designs deployed in billions of devices worldwide.
Science Advisor
Prof. James Demmel
Distinguished Professor at UC Berkeley; member of NAE, NAS, AAAS; recipient of Kanellakis, Fernbach, Babbage, and Wilkinson awards; fellow of IEEE, ACM, AAAS, AMS, SIAM. World-renowned authority in linear algebra, numerical computation, and communication-avoiding algorithms.
Architecture Advisor
Bill Huffman
Former Chief Architect at Cadence/Tensilica, where he led the development of Xtensa—the industry’s first extensible instruction set architecture (ISA). Contributor to RISC-V Vector Extension (RVV), and inventor of 34 patents in areas of memory systems, caching, coherence, and more.
Business Advisor
Jerry Ardizzone
Former VP of Worldwide Sales at ARM, Tensilica, SiFive, Cadence (IP sales). Led global commercialization efforts for companies of all sizes, securing hundreds of millions of dollars in licensing deals over a 40+ year period.
Business Development Advisor
Dr. Hakan Umit
Business development executive driving startups from inception to multi-million-dollar valuations, securing $50M+ in government contracts for deep tech ventures, and forging strategic partnerships with investors and large corporations.
Founding Engineer
Sudhanva Kulkarni
Research and Teaching Assistant at UC Berkeley. Investigated mixed-precision linear solvers using compact formats to advance efficient linear algebra for AI and HPC applications.
