DISSERTATION DEFENSE

Author : Iftakhar Ahmad
Advisors: Dr. Jason Bakos
Date: October 23, 2026
Time: 11:00 am
Location: Seminar room 2277, Storey Innovation Center
Link:  https://teams.microsoft.com/meet/276223938519678?p=lH74ckYG9mZET45s5D

Abstract

Cloud-based deep learning models are experiencing an era of unprecedented improvement in capability and adoption, driven in large part by corresponding growth in cloud-based infrastructure. However, models that are deployable on unconnected embedded systems are seeing a substantially slower pace of growth. This is a critical capability gap because embedded models, especially those that can operate in real-time, have the potential to revolutionize autonomous systems. One of the challenges inherent in improving capability in locally-executed models is their asymptotic tradeoff between accuracy and cost. Additionally, neuromorphic architectures also exhibit an asymptotic tradeoff between performance and scale. Co-optimizing model accuracy and the capabilities delivered by an underlying target platform is still an open problem, which requires exploration of both the hyperparameter and hardware parameter spaces. Specifically, Deep Neural Network (DNN) models like MobileNet, YOLO, and Vision Transformers (ViT) offer great promise in real-time applications. Such models must be tuned carefully to meet the constraints of the embedded platforms while also finding a practical compromise between performance and cost in terms of resource usage and energy expenditure. This dissertation will develop tools to perform constrained multi-objective optimization of large DNN models along with their corresponding target platforms. The proposed tools will develop objective functions that combine model accuracy and execution time while constraining memory and power requirements. The proposed tools will derive model accuracy using Bayesian optimization over the space of hyperparameters and will model platform performance using a roofline model. The proposed framework will also generate an optimal Pareto front of network and device configurations to meet real-time constraints. The outcome of this dissertation will be a generalized hardware-software co-optimization framework that can take in any large DNN model and optimize it for a given hardware.