Research
We develop machine learning (ML)-based methodologies for the design of advanced computing systems using emerging technologies to enable energy-efficient, high-performance, and reliable inference and training of emerging ML models.
Our research program aims to solve the full range of problems to design the next generation of advanced computing systems, from edge devices and mobile platforms to high-performance computing (HPC) in servers and data centers for AI workloads. We identify unique design challenges and trade-offs in emerging compute platforms. This understanding allows us to create surrogate mathematical/ML models that capture the complexity of micro-architectures, the dataflow between cores (data communication patterns), etc. Next, we use these validated models to design novel ML-based methodologies to solve multi-
objective design space exploration/optimization and runtime resource management, targeting energy efficiency, performance, and reliability. Our research focuses on cross-layer hardware and software co-design spanning across applications, advanced packaging, and architecture – synergistically combining the strengths of ML and emerging compute technologies.
Computing with Emerging Substrates
This thrust investigates design automation (EDA) challenges due to emerging non-ideal compute technologies.
Neuromorphic Computing
Computing at the edge presents new challenges due to limited battery and memory storage, requiring ultra-low-power design and parameter-efficient neural networks. Neuromorphic computing has the potential to reduce the energy consumption by 100×, specifically for event-driven tasks such as gesture and motion recognition.
Reliability in Advanced Packaging
In addition to power and performance objectives, we consider package reliability aspects, such as IR drop, warpage, mechanical stress, and electromigration (EM). These reliability issues can cause chip failure in mission-critical scenarios. For instance, package warpage causes stress in micro-bumps between the chiplet and interposer, causing cracks and voids over time. This requires careful design and co-optimization of the architecture and package.
Collaborations
We are looking for amazing collaborators for including but not limited to datacenter, agriculture, and space applications.