Rebellions Boosts Peta-Scale AI Inference with proteanTecs Monitoring
proteanTecs®, a world chief in deep data solutions for electronics well being and efficiency monitoring, introduced at this time that Rebellions, a cutting-edge AI semiconductor firm, has adopted proteanTecs’ embedded lifecycle monitoring analytics for its purpose-built AI accelerators, designed for inference at scale.
Rebellions’ AI accelerators are engineered for the brand new period of large-scale AI. Supporting workloads from LLMs to multi-modal purposes, the corporate’s superior chiplet structure, ultra-high-bandwidth HBM3E integration, and optimized software program stack allow hyperscale deployment with breakthrough vitality effectivity and efficiency. Rebellions is a number one semiconductor firm delivering each chiplet-based designs and HBM3E into manufacturing, powering peta-scale mixture-of-experts (MoE) inference and setting new benchmarks for AI infrastructure.
By embedding proteanTecs’ monitoring IP and leveraging its ML-driven analytics, Rebellions beneficial properties real-time chip telemetry insights into their techniques’ efficiency and well being. This allows vital energy discount, sooner time-to-market, and enhanced high quality and reliability.
“Our mission is to ship next-generation AI processors that stability efficiency and cost-efficiency,” stated Jinwook Oh, Co-founder and CTO at Rebellions. “proteanTecs offers us deeper visibility into our chips, from product ramp via quantity manufacturing and into datacenter deployment. Their deep information monitoring options permit us to push performance-per-watt even additional whereas guaranteeing resilience at scale.”
“As AI adoption accelerates globally, balancing efficiency beneficial properties with vitality effectivity and system robustness is vital for cloud-scale deployments,” stated Sanjay Lall, Chief Revenue Officer at proteanTecs. “Rebellions is driving a brand new normal in AI acceleration with its high-performance, high-bandwidth, chiplet-based designs. We’re proud to help their innovation as they efficiently scale to fulfill probably the most demanding workloads.”
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