#Hardware Acceleration

SkewAdam: Rethinking MoE Optimizer Memory

SkewAdam drastically cuts MoE training memory by tailoring optimizer state to parameter populations, achieving superior perplexity and enabling training on accessible hardware.

Edge AI Acceleration Gets Flexible

Researchers developed a novel FPGA-based accelerator that dynamically adjusts neural network precision at runtime, boosting inference speed for edge AI.

Arm at NeurIPS 2025: Efficiency Over Scale in AI

Inside Ironwood AI Stack: Google's Bet on Co-Design for Scale

DualBird Funding Aims to Supercharge AI Data Processing in the Cloud

QuamCore Secures $26M for 1M-Qubit Quantum Computer

QuamCore secured $26 million in Series A funding, led by Sentinel Global. This investment aims to develop a one-million-qubit quantum computing system. The round brings QuamCore's total funding to $35 million.

Making Machine Learning Inference Meet Real-World Performance Demands

FPGAs offer the configurability needed for real-time machine learning inference, with the flexibility to adapt to future workloads. Making these advantages accessible to data-scientists and developers calls for tools that are both comprehensive and easy to use. Daniel Eaton, Sr Manager, Strategic Marketing Development, Xilinx