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Dr. Shreejith Shanker

Assistant Professor (Electronic & Elect. Engineering)
ARAS AN PHIARSAIGH
      
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Dr. Shreejith Shanker

Assistant Professor (Electronic & Elect. Engineering)
ARAS AN PHIARSAIGH

 SHREEJITH.SHANKER@tcd.ie

 3531896 1609


Dr. Shanker is an Assistant Professor at the Department of Electronic & Electrical Engineering at Trinity College Dublin, The University of Dublin, Ireland, since April 2019. He graduated with a Bachelors degree in Electronics & Communication Engineering from the former the University of Kerala in 2006 and a PhD degree from Nanyang Technological University and Technical University of Munich in 2016. From 2015 to 2016, he was a post-doctoral research fellow at the Hardware and Embedded Systems Lab, Nanyang Technological University, Singapore where he was working on cognitive radio architectures and techniques for commercial and aeronautical communication systems. He took up the role of Teaching Fellow at the School of Engineering, The University of Warwick, UK in 2017, where he continued his research on in-network computation and accelerators, while primarily focusing on delivering modules on Computer Architecture and Programming to the undergraduate cohort. He briefly took up the role of Research Fellow at the Electrification Suite and Test Lab , TUM CREATE Ltd, Singapore, exploring research ideas on decentralised compute systems in smart energy systems and power grids before taking up the role of Assistant Professor at Trinity College Dublin, Ireland. His current research explores reconfigurable architectures and frameworks for distributed accelerators that are tightly coupled to the network fabric, with application to autonomous systems, media processing and communication networks. He started his professional career in 2006 as a Design Engineer at Processor Systems India where he was involved in design and verification of high-speed custom logic for network switches and compute accelerators. Later, he joined the Vikram Sarabhai Space Centre, one of the premier research centres under the Indian Space Research Organisation as a Scientist working on design of real-time, mission critical subsystems for launch vehicles and satellite systems.
  Applied Electronics   ARTIFICIAL NEURAL NETWORKS   Automotive Electronics   Communication engineering, technology   Communication Systems   Communications engineering   Computer architecture   Computer Hardware   Computer Networks   Computer/Data/Network Security   Data protection, storage technology, cryptography   Digital Computers/Computing   Digital systems, representation   Distributed systems   Electrical Engineering/Electronics   Electronic circuit design   Electronic Engineering, circuit design   ELECTRONICS   Embedded computing   Field Programmable Gate Arrays (FPGAs)   High Performance Computing   Information/Communication Systems   Integrated Circuits   Intelligent Vehicles   Network technology, Security   Networking   Networks and telecommunications research   NEURAL NETWORKS   Reconfigurable Computing   Signal processing   Systems Engineering   Vehicle technology   Very Large Scale Integration (VLSI)   Wireless Networks
Project Title
 Harmonic-AI
From
01/04/2025
To
31/03/2027
Summary
Harmonic-AI is a project led by Dr. Shreejith Shanker , with co-lead Prof. Biswajit Basu, aiming to address stability concerns arising from the widespread adoption of solar photovoltaic systems and electric vehicle (EV) charging. The research focuses on developing low-power, efficient AI models to predict the impact of renewable generation and dynamic EV charging, leading to the creation of an automated smart controller for optimised energy flow without costly infrastructure upgrades.
Funding Agency
Sustaintable Energy Authority of Ireland
Programme
SEAI RDD 2024
Project Type
RDD
Person Months
144
Project Title
 Light-weight Distributed Intrusion Detection for Automotive Networks
From
01/04/2021
To
Summary
Modern vehicles are complex machines driven by electronics, sensors and software. Increasingly, they have become targets for malicious code injections and attacks. Connectivity within a vehicle, predominantly based on legacy networks like CAN, has not evolved to cater to such attacks, while complex rule-based attack detection and prevention systems are inefficient (cost, energy etc). Deep Learning (DL) based solution offers a promising route - however, the computational complexity of DL networks needs to be addressed. This project explores a system-level architecture that enables DL-based intrusion detection to be integrated seamlessly into existing automotive networks. The key challenges we are trying to solve is the low-latency requirements for line-rate detection and the energy overheads of DL-based methods.
Funding Agency
TCD Internal
Project Title
 AI and Process Automation for Sustainable Entertainment and Media
From
To
Summary
EMERALD is a 30-month IA to develop and demonstrate exemplary tools for the digital entertainment and media industries using AI Machine Learning and Big Data technologies, to automate and speed processing, increase production efficiency, use less energy and increase the quality of content. There is a massive increase in the volume of video-based and extended reality content, with an unsustainable demand for skilled human resources, data processing and energy. EMERALD aims to meet the challenge by developing process automation for sustainable media creation; creating a testbed for measuring the energy used in media computation; developing more efficient data use for AI/ML in entertainment and media applications; reducing the power demands for large-scale media data processing; and creating acceptance and demand for AI and sustainable production technologies in the entertainment and media industries. The interdisciplinary Consortium of seven partners includes leading companies from the movie, broadcast, streaming and live entertainment technology sectors, supported by two major European universities.
Funding Agency
HORIZON-IA
Programme
HORIZON-CL4-2022-DIGITAL-EMERGING-02
Project Type
Innovation Action
Project Title
 Brain Health Evaluation using Machine Learning on Ear-EEG Data
From
01/09/2024
To
Summary
Neurological disorders are the second highest cause of death globally, including Alzheimer"s Disease (AD), the most common neurodegenerative disease and most prevalent form of dementia. In Ireland, approximately 64,000 people live with dementia, projected to rise to 150,000 by 2050. While neurodegenerative diseases are incurable, research suggests that modifying key lifestyle factors including smoking and alcohol intake could delay or prevent 40% of dementias. The brain-age gap, the difference between predicted and chronological brain age, has been proposed as a tool for assessing brain health. Mild Cognitive Impairment (MCI), a preclinical stage of AD, results in an increased brain-age gap of +6.2 years, suggesting that the brain-age gap can give an early indication of AD. However, this approach relies on MRI imaging which is expensive and infeasible for continuous monitoring. Traditional scalp electroencephalography (scalp-EEG) has been shown to identify AD with an accuracy of 90% using a Support Vector Machine (SVM) model. However, scalp-EEG would still be unsuitable for regular monitoring of brain health as it requires a specialist to perform. The proposed research explores Ear-EEG as a potential alternative to scalp-EEG, with studies showing it can predict a significant portion of scalp-EEG data. As it is possible to reconstruct scalp-EEG data from ear-EEG using various ML models and scalp-EEG readings can be used to identify brain degeneration such as AD, it follows that ear-EEG could be an alternative tool for assessing brain health.
Funding Agency
Trinity Doctorate Research Award
Programme
Trinity Doctorate Research Award
Project Title
 Resource-efficient Deep-Learning for Microwave Breast Image Reconstruction
From
To
Summary
In this project, the use of machine learning for microwave breast image reconstruction is to be explored. Specifically, this project will examine two key questions: firstly, the feasibility of directly learning a microwave breast imaging reconstruction algorithm that is generalisable and stable; secondly, the development of a custom digital design for a resource-efficient implementation of a fully learned reconstruction algorithm. Together, these questions help assess if recent developments in medical imaging could help accelerate the translation of highly efficient microwave breast imaging to clinical use
Funding Agency
TCD
Programme
Ussher Fellowship

Page 1 of 2
Details Date
Reviewer for IEEE (TVT, TCAS, OJCAS), Springer CSSP journals, ACM TRETS
Reviewer and TPC member for International Conferences on FPT, FPL, DATE, ASD and ANCS Conferences.
Language Skill Reading Skill Writing Skill Speaking
English Fluent Fluent Fluent
Malayalam Fluent Fluent Fluent
Details Date From Date To
Member, Association for Computing Machinery 2023
Walsh A, Shanker S, Valdes AL, Classifying Neurodegenerative Diseases from Selected Temporal EEG Electrodes: Towards Ear-EEG Applications., Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2025, Conference Paper, PUBLISHED  DOI
Li, Changhong and Khandelwal, Shashwat and Shanker, Shreejith, FAV-NSS: An HIL Framework for Accelerating Validation of Automotive Network Security Strategies, 2025 IEEE 36th International Conference on Application-specific Systems, Architectures and Processors (ASAP), International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2025, pp25--32 , Conference Paper, PUBLISHED
Khandelwal, Shashwat and Petri-Koenig, Jakoba and Preu{\ss, FINN-GL: Generalized Mixed-Precision Extensions for FPGA-Accelerated LSTMs, International Conference on Field Programmable Logic and Applications (FPL), 2025, Conference Paper, PUBLISHED
Kumar, Vineet and Kumar, Ajay and Li, Yike and Shanker, Shreejith and John, Deepu, Bare-Metal RISC-V+ NVDLA SoC for Efficient Deep Learning Inference, 2025 IEEE 38th International System-on-Chip Conference (SOCC), International System-on-Chip Conference (SOCC), 2025, pp1--5 , Conference Paper, PUBLISHED
Li, Changhong and Bled, Clemont, ReTiDe: Real-Time Denoising for Energy-Efficient Motion Picture Processing with FPGAs, Proceedings of the 22nd ACM SIGGRAPH European Conference on Visual Media Production, ACM SIGGRAPH European Conference on Visual Media Production, 2025, pp1--10 , Conference Paper, PUBLISHED
Vibhoothi, Vibhoothi and Zouein, Julien and Shreejith, Shanker and Kempf, Jean-Baptiste and Kokaram, Anil, An Empirical Study of Reducing AV1 Decoder Complexity and Energy Consumption via Encoder Parameter Tuning, Picture Coding Symposium, Aachen, 2025, Conference Paper, PUBLISHED
Wadhwa, Eashan and Shreejith, Shanker, Simopt-Power: Leveraging Simulation Metadata for Low-Power Design Synthesis, 2025 IEEE Nordic Circuits and Systems Conference (NorCAS), IEEE Nordic Circuits and Systems Conference (NorCAS), 2025, pp1--6 , Conference Paper, PUBLISHED
Li, Changhong and Basu, Biswajit and Shanker, Shreejith, LogicSparse: Enabling Engine-Free Unstructured Sparsity for Quantised Deep-learning Accelerators, International Conference on Field Programmable Technology (ICFPT), 2025, Notes: [International Conference on Field Programmable Technology (ICFPT)], Conference Paper, PUBLISHED
Anneliese Walsh, Shreejith Shanker, Alejandro Lopez Valdes, Multiclass Differentiation of Dementia Subtypes Based on Low-Density EEG Biomarkers: Towards Wearable Brain Health Monitoring, Journal of Dementia and Alzheimer's Disease, 2, (4), 2025, p48 , Journal Article, PUBLISHED  DOI
Khandelwal, Shashwat and Shreejith, Shanker, SecCAN: An Extended CAN Controller with Embedded Intrusion Detection, IEEE Embedded Systems Letters, 2025, Journal Article, PUBLISHED
  

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Award Date
Best Paper Award - IEEE COINS, 2022 for our paper titled "FPGA-based Deep-Learning Accelerators for Energy Efficient Motor Imagery EEG classification" 2022
My research area focuses on building computer architectures that enable unique ways for improving compute efficiency, network performance and provide reactive capabilities to adapt to changing environments, through seamless interaction of software and hardware. My research applies this approach to tailor compute, network and deep-learning architectures to enable data-driven real-time reactive solutions in different domains such as automotive embedded systems, cognitive radio systems, and biomedical systems. A key enabler for his research is fully programmable platforms (or reconfigurable hardware), which enables both the software and the underlying hardware to be adapted to the compute requirements and specifications, either statically (i.e., at design time) or dynamically (i.e., at run-time). My current research direction focuses on enabling energy-efficient ways to perform compute-intensive data-driven tasks such as (edge-) cloud analytics or deep learning inference by optimising different components of the system - from low-level computational building blocks that enable efficient offload of compute-intensive tasks, to the software APIs that interface with the accelerators, and compiler tools to automate the development and deployment of these solutions. This combined strategy enables right-sizing of operations, interconnection, storage and data movement, which are critical components in reducing the energy footprint of such data-intensive tasks. In our current research, we are exploring three key application areas - secure connected automotive systems, bio-information systems for smart health, and high-performance video streaming/processing pipelines for visual algorithms in cloud/on-premise. Additionally, we also explore decentralisation of these compute tasks and consensus schemes to enable novel applications that preserve privacy in sensitive data-driven tasks to augment our approach.