Power Gating Enabling in NPUs (Univ. of Illinois Urbana-Champaign)


A new technical paper titled "ReGate: Enabling Power Gating in Neural Processing Units" was published by researchers at the University of Illinois Urbana-Champaign. Abstract "The energy efficiency of neural processing units (NPU) is playing a critical role in developing sustainable data centers. Our study with different generations of NPU chips reveals that 30%–72% of their energy consump... » read more

Workload-Specific Hardware Accelerators


Workload-specific hardware accelerators are becoming essential in large data centers for two reasons. One is that general-purpose processing elements cannot keep up with the workload demands or latency requirements. The second is that they need to be extremely efficient due to limited electricity from the grid and the high cost of cooling these devices. Sharad Chole, chief scientist and co-foun... » read more

Chiplet-Based NPUs to Accelerate Vehicular AI Perception Workloads


A new technical paper titled "Performance Implications of Multi-Chiplet Neural Processing Units on Autonomous Driving Perception" was published by researchers at UC Irvine. Abstract "We study the application of emerging chiplet-based Neural Processing Units to accelerate vehicular AI perception workloads in constrained automotive settings. The motivation stems from how chiplets technology i... » read more

Application-Optimized Processors


Executing a neural network on top of an NPU requires an understanding of application requirements, such as latency and throughput, as well as the potential partitioning challenges. Sharad Chole, chief scientist and co-founder of Expedera, talks about fine-grained dependencies, why processing packets out of order can help optimize performance and power, and when to use voltage and frequency scal... » read more