LATEST IN THE NEWS

November 20, 2025

Vellex Computing Pitches at 2025 Tough Tech Week Demo Day in Boston

Meghesh Saini
Vellex Computing presented its analog computing technology at the 2025 Tough Tech Week Demo Day in Boston, pitching to deep-tech investors and founders alongside nearly 100 startups from across the science and engineering ecosystem.
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September 16, 2025

Vellex Computing Receives NSF SBIR Award for Analog Computing Research in Optimization and AI

Meghesh Saini
Vellex Computing has been awarded a $305,000 NSF SBIR Phase I grant to develop its analog computing technology for real-time optimization, with applications spanning power systems simulation, on-device AI training, and real-time edge control.
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September 10, 2025

Vellex Computing Pitches at Plug and Play Silicon Valley Summit

Meghesh Saini
Vellex Computing presented at Plug and Play's Silicon Valley Summit in Sunnyvale, where CEO Dr. Palak Jain pitched the company's physics-based analog computing technology to deep-tech investors and industry leaders from over 20 sectors.
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OUR BLOGS

May 28, 2026

Analog vs. Digital: The Ultimate Guide to Choosing the Right Tech

Vedant Wakchaware
Analog vs. Digital technology is a decades-old debate, but which one is actually better for the future of tech? In this comprehensive guide, we break down the core differences between continuous analog waves and discrete digital steps. Discover the unique pros and cons of each system, learn which architecture is best suited for your specific industry—from professional audio to cutting-edge AI—and explore how modern hardware engineers are blending both into powerful, ultra-efficient Hybrid (A/D/A) systems to overcome the massive energy limitations of today's devices.
May 8, 2026

The Hidden Economics of AI: Why Tokens Are Costing Millions in Training and Usage

Vedant Wakchaware
Tokens are the invisible currency of artificial intelligence, and processing them carries a staggering hidden cost. Why does training a frontier model require gigawatt-hours of electricity, while developers face unexpected API bills? Step inside this foundational metric to decode these massive economics. This deep dive breaks down the physical infrastructure required to process trillions of data fragments—from specialized GPU clusters to liquid cooling taxes. Discover the invisible multipliers inflating your usage, and learn comprehensive optimization strategies like smart routing and prompt caching to drastically reduce your AI costs.
April 30, 2026

A Detailed Guide to Federated Learning on Edge Devices

Vedant Wakchaware
While on-device training secures user privacy, it unintentionally traps intelligence, forcing every edge device to learn the exact same lessons from scratch. How do we build a collaborative "hive mind" without exposing raw data to the cloud? The answer is Federated Learning. This comprehensive guide explores the decentralized paradigm of bringing the model to the data, detailing how devices evolve together by sharing abstract mathematical updates. Dive into the 5-step federated architecture loop and discover how cryptographic shields like Secure Aggregation and Differential Privacy prevent data extraction. Learn how advanced algorithms overcome severe bandwidth constraints and hardware disparities to power the next generation of secure, collective AI.

OUR PUBLICATIONS

December, 2021

Decentralized Carrier Phase Shifting for Optimal Harmonic Minimization in Asymmetric Parallel-Connected Inverters

Jason Poon; Brian Johnson; Sairaj V. Dhople; Juan Rivas-Davila
Published in IEEE Transactions on Power Electronics ( Volume: 36, Issue: 5, May 2021)
September, 2021

A Digital Twin Approach for Fault Diagnosis in Distributed Photovoltaic Systems

Palak Jain; Jason Poon; Jai Prakash Singh; Costas Spanos; Seth R. Sanders; Sanjib Kumar Panda
Published in IEEE Transactions on Power Electronics ( Volume: 35, Issue: 1, January 2020)
August, 2021

Model-Based Fault Detection and Identification for Switching Power Converters

Jason Poon; Palak Jain; Ioannis C. K.; Costas Spanos; Sanjib K. Panda; Seth R. Sanders
Published in IEEE Transactions on Power Electronics ( Volume: 32, Issue: 2, February 2017)