Seun Alo

My name is Seun Alo, and I am a Ph.D. candidate in Electrical Engineering at the University of Kentucky, specializing in computer architecture and hardware acceleration. I am currently conducting my research at the Unconventional Computing Architectures and Technologies (UCAT) laboratory, where I focus on designing scalable and energy-efficient hardware accelerators for Geometric Deep Learning applications. My work explores innovative dataflows and emerging technologies such as photonic computing to enable high-performance, low-power machine learning architectures. In April 2023, I successfully passed my Ph.D. oral qualifying examination, presenting a proposal on “Design of an Efficient, Scalable, and Flexible Tensor Processing Architecture with Photonic Integrated Circuits.”

Prior to starting my Ph.D., I earned a Master’s degree in Electrical and Electronic Engineering from the University of Ibadan, Nigeria in 2019, and a Bachelor’s degree in the same field from Federal University of Akure in 2014. Alongside my academic journey, I gained industry experience working in different engineering positions and finally as a Service Delivery Project Manager at Huawei Technologies Nigeria from 2017 to 2022, where I led technical delivery operations and stakeholder coordination in large-scale telecommunications projects, before heading back to Academia for a short career break.

Now, as I continue my doctoral studies, I am driven by the goal of bridging the gap between next-generation computing architectures and real-world machine learning applications. My mission is to develop intelligent, efficient, and adaptable systems that meet the demands of tomorrow’s computing challenges.

Education

Ph.D. in Electrical Engineering, University of Kentucky, Lexington, KY (2022 – Present)
M.Sc. in Electrical/Electronic Engineering, University of Ibadan, Nigeria
B.Eng. in Electrical/Electronic Engineering, Federal University of Akure, Nigeria

Professional Experience

Service Delivery Project Manager
Huawei Technologies, Nigeria | 2017 – 2022

Managed service operations, technical issue resolution
Other positions on my LinkedIn

Research Experience

Graduate Researcher, UCAT Lab
University of Kentucky | 2022 – Present

Working on hardware accelerators for Geometric Deep Learning.
Passed Ph.D. qualifiers: “Design of an Efficient, Scalable, and Flexible Tensor Processing Architecture with Photonic Integrated Circuits” (April 2023).
Currently working on publications on GEMM Accelerators for Neural Networks.

Research Assistant
University of Ibadan | 2016 – 2019

Developed Content-Based Image Retrieval Systems using Color, Edge, and Texture features.

Research Interests

Computer Vision
Artificial Intelligence (Machine/Deep Learning) **
Computer Architecture and Embedded systems **
VLSI Design and Technology
Signal/Image Processing and Communication

Projects

Current: Scaling Analog Photonic Accelerators for Integer GEMM Kernels

This project explores the design and optimization of analog photonic accelerators targeted at byte-sized (8-bit) integer matrix multiplication operations — a foundational component in many deep learning applications. The work focuses on achieving high-speed, low-energy multiply-accumulate (MAC) operations by integrating photonic computing elements. Challenges addressed include scaling the photonic hardware while maintaining signal integrity and low power consumption.

The SPOGA Architecture

GLCM-Based Image Texture Analysis

Implemented a content-based image retrieval system using second-order statistical features derived from the Grey Level Co-occurrence Matrix (GLCM). The system evaluates texture information for image classification and retrieval tasks. It improves upon traditional CBIR techniques by leveraging GLCM parameters like contrast, correlation, energy, and homogeneity.

Publications

S. Afifi, O. A. Alo, I. Thakkar, and S. Pasricha,
“ASTRA: A Stochastic Transformer Neural Network Accelerator with Silicon Photonics,”
In Progress
O. A. Alo, S. S. Vatsavai, and I. Thakkar,
“Scaling Analog Photonic Accelerators for Byte-Size, Integer General Matrix Multiply (GEMM) Kernels,”
in Proc. IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Knoxville, TN, USA, Jul. 2024, pp. 409–414.
https://doi.org/10.1109/ISVLSI61997.2024.00080
V. S. P. Karempudi, S. S. Vatsavai, I. Thakkar, O. A. Alo, J. T. Hastings, and J. S. Woods,
“A Low-Dissipation and Scalable GEMM Accelerator with Silicon Nitride Photonics,”
arXiv preprint, arXiv:2402.11047, Feb. 2024.
https://arxiv.org/abs/2402.11047
S. S. Vatsavai, V. S. P. Karempudi, O. A. Alo, and I. Thakkar,
“A Comparative Analysis of Microrings Based Incoherent Photonic GEMM Accelerators,”
in Proc. 25th Int. Symp. Quality Electronic Design (ISQED), Santa Clara, CA, USA, Apr. 2024, pp. 1–8.
https://doi.org/10.48550/arXiv.2402.03149
O. A. Alo and A. R. Zubair,
“Grey Level Co-occurrence Matrix (GLCM) Based Second-Order Statistics for Image Texture Analysis,”
Int. J. Comput. Appl., vol. 93, no. 8, pp. 64–73, 2019.
https://doi.org/10.48550/arXiv.2403.04038
O. A. Alo and A. R. Zubair,
“Content-based Image Retrieval System Using Second-Order Statistics,”
Int. J. Comput. Appl., vol. 176, no. 36, pp. 12–20, Jul. 2020.
https://doi.org/10.5120/ijca2020920475