R. Iris Bahar
I am a Professor of Computer Science and Engineering at Brown University. My research interests lie broadly in the areas of computer system design and electronic design automation. In particular, my research focuses on energy-efficient and reliable computing, from the system level to device level. Past research topics have included modelling thermal noise effects in nanoscale circuits, design of noise- and error-immune circuits, approximate computing (from systems to circuits), and memory synchronization techniques for multiprocessor systems. Most recently, my research interests have led me to explore applications for near-data processing and design of robust machine learning techniques for robot scene perception.
Concurrent Near-Data Processing Architectures
Recent advances in memory architectures have provoked renewed interest in near-data-processing (NDP) as way to alleviate the “memory wall” problem. An NDP architecture places logic circuits, such as simple processors, in close proximity to memory. This is distinct from processing-in-memory (PIM) where logic computation is effectively integrated into the memory cells/arrays.
Robust and Computationally-Efficient Scene Perception
Technological advancements have led to a proliferation of robots using machine learning systems to assist humans in a wide range of tasks. However, we are still far from accurate, reliable, and resource-efficient operations of these systems.