Compact Modeling and Mitigation of Parasitics in Crosspoint Accelerators of Neural Networks

In-memory computing (IMC) can accelerate data-intensive tasks, such as matrix-vector multiplication (MVM) or artificial neural networks (ANNs) inference, by means of the crosspoint memory array, allowing to reduce time and energy consumption. IMC accuracy, however, is affected by nonidealities, such as variability of the conductive weights or IR drop along wires due to parasitic resistances, whose impact steeply increases with the increase of array size. This work proposes a compact model to assess the impact of nonidealities for various circuital implementations, together with architectural schemes for their mitigation based on replicated arrays. The proposed mitigation techniques allow to restore the ANN accuracy from 72.7% to 94.9%, close to the software accuracy of 96.9%, in view of an increased area and energy consumption

Standardization and selection of high-risk patients for surgical wound infections in plastic surgery

Background: The aim of the present study was to show that the Infection Risk Index (IRI), based on only 3 factors (wound classification, American Society of Anesthesiologists score, and duration of surgery), can be used to standardize selection of infection high-risk patients undergoing different surgical procedures in Plastic Surgery. Methods: In our Division of Plastic Surgery at Modena University Hospital, we studied 3 groups of patients: Group A (122 post-bariatric abdominoplasties), Group B (223 bilateral reduction mammoplasties), and Group C (201 tissue losses with first intention healing). For each group, we compared surgical site infection (SSI) rate and ratio between patients with 0 or 1 risk factors (IRI score 0 or 1) and patients with 2 or 3 risk factors (IRI score 2 or 3). Results: In group A, patients with IRI score 0-1 showed an SSI Ratio of 2.97%, whereas patients with IRI score 2-3 developed an SSI ratio of 27.27%. In group B, patients with IRI score 0-1 showed an SSI ratio of 2.99%, whereas patients with IRI score 2-3 developed an SSI ratio of 18.18%. In group C, patients with IRI score 0-1 showed an SSI ratio of 7.62%, whereas patients with IRI score 2-3 developed an SSI ratio of 30.77%. Conclusions: Existing infection risk calculators are procedure-specific and timeconsuming. IRI score is simple, fast, and unspecific but is able to identify patients at high or low risk of postoperative infections. Our results suggest the utility of IRI score in refining the infection risk stratification profile in Plastic Surgery