Jeehwan Kim

This research group works on AI hardware based on memristor neural networks with emphasis on ultra-low power operation for inference and online training and 3D integration of AI hardware and Si electronics.

Vivienne Sze, Joel Emer

This project pursues an integrated framework that includes energy-modeling and performance evaluation tools to systematically explore and estimate the energy-efficiency and performance of Analog Neural Network architectures with full consideration of the electrical characteristics of the synaptic elements and interface circuits.

Luqiao Liu, Marc Baldo

Networks formed by devices with intrinsic stochastic switching properties can be used to build Boltzmann machine, which has great efficiencies compared with traditional von Neumann architecture for cognitive computing due to the benefit from statistical mechanics of building blocks.

Jesús del Alamo

This research project investigates a new ferroelectric synapse technology based on metal oxides that is designed to be fully back-end CMOS compatible and promises operation with great energy efficiency.

Bilge Yildiz, Ju Li

Electrochemical ionic-electronic devices have an immense potential to enable a new domain of programmable hardware for machine intelligence.

Anantha Chandrakasan

Many edge machine learning accelerators are responsible for processing and storing sensitive data that could be of value to attackers. This project plans to investigate side channel vulnerabilities and develop protections for at-edge custom in-memory computing (IMC) integrated circuits.

Song Han, Anantha Chandrakasan

This project aims to develop efficient processors for natural language processing directly on an edge device to ensure privacy, low latency and extended battery life. The goal is to accelerate the entire transformer model (as opposed to just the attention mechanism) to reduce data movement across layers.

Bilge Yildiz, Michale Fee, Jesus del Alamo, Ju Li

New approaches to brain-inspired computing could achieve greater than a million-fold improvements in energy efficiency.

Song Han

This project pursues efficient machine learning for mobile devices where hardware resources and energy budgets are very limited.