… This collaboration is particularly relevant for AI and HPC applications. Large language models and AI inference engines require enormous memory bandwidth and low-latency interconnects between compute and memory resources. …
… Companies that fail to account for this compounding effect risk costly post-silicon bugs, longer time-to-market, and performance inefficiencies. …
… The announcement also highlights expansion into new application domains. …
… You’re going to need multiple channels or multiple data movers to sustain the full data rate.” As with so many connectivity schemes, running one link fast is tempting, but designing a chip with more application interfaces spread out at lower clock speeds is less risky. …
… Also Read: Synopsys Announces Expanding AI Capabilities and EDA AI Leadership AI Everywhere in the Chip Lifecycle: Synopsys at AI Infra Summit 2025 Synopsys Collaborates with TSMC to Enable Advanced 2D and 3D Design Solutions Share this post via:
… Also Read: ASML High-NA EUV is Not Ready for High-Volume Production Siemens EDA Expands AI and Advanced Packaging Collaboration with TSMC imec IC-Link and TSMC 3DFabric Alliance Expansion Signals New Era of System-Level Scaling Share this post via:
… AI runs on chips. The foundation models, inference engines, training clusters — all of it requires leading-edge silicon that requires the process engineering expertise the AI industry is pulling talent away from. The demand that makes AI possible is eroding the supply chain that makes AI possible. …