Secure Machine Learning
Deep learning deployments, especially in safety- and health-critical applications, must account for the security or else malicious attackers will be able to engineer misbehavior with potentially disastrous consequences (autonomous car crashes, for instance). How we can safely and securely deploy ML/AI technology in the real-world?
Efficient Hardware for Deep Learning
Training and executing deep neural networks is computationally demanding. For this reason, leading companies are designing specialized chips to accelerate deep learning workloads. Our work explores new circuit and architectural optimizations to increase the performance, reliability and energy-efficient of deep learning hardware.
Hardware Security
Most semiconductor companies don’t the resources to manufacture computer chips. Advanced semiconductor chips are manufactured at one of a small number of “fabs” and several countries, India for example, do not have any on-shore fab. So, chip manufacturing is outsourced off-shore. But this raises a question of trust: how can we guarantee the security of off-shore chip fabrication?