Edge AI inference is an important workload in 5G networks. Multiple classes of edge AI workloads often share edge infrastructure, yet each may require distinct latency and rate guarantees. 5G network slicing supports differentiated requirements on the network side, but shared GPU inference also needs compute-side guarantees after requests reach the Multi-access Edge Computing (MEC) host. We extend 3GPP network slicing with compute-side enforcement so that slice guarantees remain effective after traffic reaches the MEC host. To realize this extension, we design a GPU scheduler that combines Hierarchical Token Bucket (HTB)-based traffic conditioning with Earliest Deadline First (EDF) scheduling. Our scheduler enforces per-class assured goodput, defined as the committed rate of latency-compliant completions for each class. The GPU scheduler identifies request classes via tags, which are assigned during GTPU encapsulation at the 5G user plane. This integration preserves overall latency guarantees across both the network and compute domains of a 5G slice for AI.

We propose a new methodology to predict minimum operating voltage (Vmin) for production chips. In addition, we propose two new key features to improve the prediction accuracy. Our proposed accumulative learning can reduce the impact of lot-to-lot variations. Experimental results on two 7nm industry designs (about 1.2M chips from 142 lots) show that we can achieve above 95% good prediction. Our methodology can save 75% test time compared with traditional testing. To implement this method, we will need to have a separate test flow for the initial training and accumulative training.

We present a novel approach for assisting pattern interpretation by data mining end-users: finding explanations for association rules based on probabilistic dependencies. In the approach, relevant variables are selected from rules and from other data sources to facilitate human-understandable interpretations. An explanation of a rule involves consideration of observable variables in the data and alignment with the conditional probability of the rule. To build explanations involving multiple, interacting variables, we use Bayesian networks to structure relationships. We illustrate the benefits of our technique for assisting pattern interpretation using Internet use survey data. The novel technique has potential in various data mining scenarios such as computer aided pattern interpretation and interactive data mining.