The ‘Tapping Mode SQUID-on-Tip’ (TM-SOT) microscope enables multimodal imaging to be performed extremely close to the sample ...
Who will lead the integration of AI with EDA? That story has not yet been written, but there are some unlikely contenders.
Researchers from Imperial College London and Bytedance released “Systems-Level Attack Surface of Edge Agent Deployments on IoT”. Abstract “Edge deployment of LLM agents on IoT hardware introduces ...
Researchers from Stanford University and University of California, Santa Cruz have released “Heterogeneous Memory Design ...
Researchers from Seoul National University and KAIST published “Oxide Semiconductor Gain Cell-Embedded Memory: Materials and Integration Strategies for Next Generation On-Chip Memory”. Abstract “The ...
Panelists repeatedly highlighted that AI compute scaling is dramatically outpacing traditional Moore’s Law transistor ...
Researchers from University of Bremen have released “Linear Formal Verification of Sequential Circuits using Weighted-AIGs”. Abstract “Ensuring the functional correctness of a digital system is ...
End-to-End Hardware-Driven Graph Preprocessing for Enhanced GNN Performance” was published by researchers at KAIST, Panmnesia ...
Researchers from Boston University, Northeastern University, KAIST, and University of Murcia, et al. have released “FHECore: Rethinking GPU Microarchitecture for Fully Homomorphic Encryption”.
AI is beginning to make inroads into designing and managing programmable logic, where it can be used to simplify and speed up portions of the design process. FPGAs and DSPs are st ...
EDA produces a lot of data, but how useful is that for AI to consume? The industry looks at new ways to help AI do a better job.
The industry’s response is to split compute, memory, and I/O across dies, XPU chiplets are pushing toward the reticle limit, and stitch it all together with high‑bandwidth, energy‑efficient die‑to‑die ...
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