No abstract

In a framework for substance use concerning trauma, Hien and colleagues suggested three domains: reward salience, executive function, and negative emotionality. In this PRISMA-guided systematic review, we explored the neural correlates of these domains in individuals who use substances with or without trauma exposure. We included 45 studies utilizing tasks of interest in alcohol, tobacco, and cannabis use groups. Greater reward, lesser regulation of inhibitory processes, and mixed findings of negative emotionality processes in individuals who use substances versus controls were found. Specifically, greater orbitofrontal cortex, ventral tegmental area, striatum, amygdala, and hippocampal activation was found in response to reward-related tasks, and reduced activation was found in the inferior frontal gyrus and hippocampus in response to inhibition-related tasks. No studies in trauma-exposed individuals met our review criteria. Future studies examining the role of trauma-related factors are needed and should explore inhibition- and negative-emotionality domains in individuals who use substances to uncover alterations in these domains that place an individual at greater risk for developing SUD.

The integration of FPGAs into large-scale computing systems is gaining attention. In these systems, real-time data handling for networking, tasks for scientific computing, and machine learning can be executed with customized datapaths on reconfigurable fabric within heterogeneous compute nodes. At the same time, thermal management, particularly battling the cooling cost and guaranteeing the reliability, is a continuing concern. The introduction of new heterogeneous components into HPC nodes only adds further complexities to thermal modeling and management. The thermal behavior of multi-FPGA systems deployed within large compute clusters is less explored. In this article, we first show that the thermal behaviors of different FPGAs of the same generation can vary due to their physical locations in a rack and process variation, even though they are running the same tasks. We present a machine learning–based model to capture the thermal behavior of each individual FPGA in the cluster. We then propose two thermal management strategies guided by our thermal model. First, we mitigate thermal variation and hotspots across the cluster by proactive thermal-aware task placement. Under the tested system and benchmarks, we achieve up to 26.4° C and on average 13.3° C system temperature reduction with no performance penalty. Second, we utilize this thermal model to guide HLS parameter tuning at the task design stage to achieve improved thermal response after deployment.