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Low Power-High Performance >
Simplify Electronic-Photonic Design Workflows: eBook
Low Power-High Performance

WHITEPAPERS

The natural evolution of EDA, extending proven design methodologies to include photonic devices, circuits, and systems.
October 14th, 2026 -
By: Synopsys

Combining light-based components with traditional electronics is not straightforward. Conventional electronic design automation (EDA) workflows were never built to handle the complex physics and deep integration demanded by photonics. As optical and electronic technologies converge, engineers face tighter cross-domain coupling and unprecedented system-level complexity.
Addressing these challenges requires a new design paradigm: electronic photonic design automation (EPDA).
Driven by exploding bandwidth demands, power and latency constraints, and the rapid adoption of silicon photonics, EPDA is no longer optional — it is essential for delivering scalable, manufacturable electronic photonic systems.
Read more here.
- Agentic AI Automates RTL Waveform Analysis And Debugging (Columbia, Google) October 13, 2026 by Technical Paper Link
- Carbon-Dot Interface Enables Ultrathin High-κ Dielectrics on 2D Semiconductors (UIUC, NETL, Argonne, Monash) October 12, 2026 by Technical Paper Link
- On-Chip Monitors Detect Bit-Flip Attacks in AI Accelerators (UF, UT Dallas, RPI) October 12, 2026 by Technical Paper Link
- Deterministic Nucleation Enables Controlled Growth of 2D Semiconductors (KAIST, TDS, Hanyang et al.) October 12, 2026 by Technical Paper Link
- Hyper-NA EUV: The Next Extension Beyond High NA Lithography (Carl Zeiss, ASML) October 7, 2026 by Technical Paper Link
As AI clusters push beyond rack-scale limits, optical interconnects and circuit switching are reshaping how data centers scale.

by Bryon Moyer

Is it better to cram more compute into each rack or rethink the architecture?

The next power bottleneck is no longer just inside the accelerator — it is the full conversion path from medium-voltage AC to sub-1V silicon.

by Liz Allan

As the semiconductor ecosystem pivots to AI, it is transforming how IP is created, verified, managed, and sold.

No system can be optimized without an understanding of both the hardware and software, and making the right architectural choice requires cooperation.

by Brian Bailey

Power, token cost, interconnects, and software orchestration are pushing data centers toward heterogeneous clusters built from CPUs, GPUs, NPUs, optics, and custom accelerators.

For compute-intensive applications, clusters of nodes that act as a single computer offer a way to scale performance and provide a workaround for organizations that lack access to leading-edge AI chips.

Redundant feeds protect uptime, but they also strand capacity that could support more hardware.

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Generative AI On A General-Purpo... Quadric.io
Simplify Electronic-Photonic Des... Synopsys