Built by engineers. Driven by precision.
Ideas To Design Systems was founded to solve the hardest hardware design challenges at hyperscale — combining deep engineering expertise with AI-accelerated workflows.
Accelerating the hardware design that powers the future
The world's most demanding infrastructure — hyperscale cloud, AI accelerators, enterprise data centers — runs on hardware that must be right the first time. At i2d Systems, we exist to make that possible.
We combine decades of systems engineering experience with AI-powered design tools to deliver architecture, schematics, PCB layouts, and bring-up support that meet the most rigorous performance and reliability standards.
Principles that guide every design decision.
First-Pass Success
We design for correctness from the start — rigorous architecture implementation, verification, signal integrity analysis before a single board is fabricated.
Engineering Depth
Our team brings hands-on experience across high-speed SerDes, power delivery networks, thermal management, and complex multi-board systems.
AI-Accelerated Delivery
We leverage AI tools to compress design cycles without sacrificing quality — delivering faster iterations and more thorough verification.
Client Partnership
We embed with your team, align to your roadmap, and stay engaged through bring-up and validation — not just tape-out.
The engineers behind i2d Systems.
William Xie
William is a 29-year veteran of hardware systems design. At Amazon Web Services (AWS), he led the Board Core Design team within the Scaled Compute organization, overseeing board, power, and signal-integrity design for multiple generations of AWS compute and accelerator products. He has held director and senior hardware engineering leadership roles at AWS, Luminous Computing, Flex Logix, and HPE/3PAR, guiding the architecture and delivery of some of the industry's most demanding compute and enterprise storage platforms.
Chris Cheng
30+ year veteran in SI/PI systems design. HPE/3PAR Distinguished Technologist and Intel Principal Engineer. Founding sponsor and chairman of the Advisory Board for CAEML (Center for Advanced Electronics Through Machine Learning) Consortium. DesignCon AI/ML Track Chair.
David Pan
Silicon Labs Endowed Chair Professor at UT Austin and Director of the UT Design Automation Lab. ACM, IEEE, and SPIE Fellow. DAC Chair. Nvidia Faculty Research Fellow and advisor to Google DeepMind and Google X.