December 2021
Data communication plays a significant role in overall performance for hardware accelerators of Deep Neural Networks (DNNs). For example, crossbar-based in-memory computing significantly increases on-chip communication volume since the weights and activations are on-chip. State-of-the-art interconnect methodologies for in-memory computing deploy a bus-based network or mesh-based NoC.
The power system has undergone significant changes and improvements. One of the most important ongoing revolutions is the emergence of wide bandgap (WBG) semiconductors.
Though the potential of artificial intelligence (AI) in healthcare warrants genuine enthusiasm, meaningful impact will require careful integration into clinical care. AI tools are susceptible to mistakes and rarely capable of capturing all of the nuances pertaining to a complex clinical situation. Thus, we propose approaches designed to augment, rather than replace, clinicians during clinical decision making. In this talk, Associate Professor Jenna Wiens will highlight three related research directions pertaining to: i) a transfer learning approach for mitigating potentially harmful shortcuts when making diagnoses, ii) a simple yet accurate deterioration index that generalizes across hospitals and iii) lessons learned during deployment of a risk stratification tool for predicting healthcare-associated infections. In summary, there’s a critical need for machine learning in healthcare; however, the safe and meaningful adoption of these techniques will require collaboration between clinicians and AI.
As you work on your final year of engineering courses, chances are you’re probably also trying to figure out how to get your engineering career started after graduation. In my talk I’ll share some things I learned as an engineer and a hiring engineering manager from applications and resumes to preparing for your interview.