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.

Contrary to expectations, the experiment revealed that the respondents held on to their views firmly, regardless of the celebrity inputs or their esteem in the eyes of the respondents. It was also clear that respondents liked to hear an opinion identical to their own even if it came from a disliked celebrity. Conversely, a dissenting opinion by a celebrity or expert reduced the respondent’s empathy for that person.

Two EPFL students have developed PowerSGD, an algorithm that allows compression of the needed bandwidth without compromising the accuracy of the training.