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Simla Burcu Harma, a researcher on the ColTrain project, has received the Generation Google Scholarship for Women in Computer Science. @SimlaBurcu

DNN training and inference have similar basic operators but with fundamentally different requirements. The former is throughput bound and relies on high precision floating-point arithmetic for convergence while the latter is latency-bound and tolerant to low-precision arithmetic. Both workloads require high computational capabilities and can benefit from hardware accelerators. The disparity in resource requirements forces datacenter operators to choose between custom accelerators for training and inference or training accelerators for inference.

In their paper, the researchers introduce a nanowire-based device to create high-electron-mobility tri-gate transistors for power-conversion applications. Based on nanoscale structures, the novel transistor design significantly reduces heat loss during the energy conversion process.