Guwahati (Assam) [India], September 4 (ANI): Indian Institute of Technology Guwahati (IIT Guwahati) researchers are working on developing a novel brain-inspired Artificial Intelligence model designed to efficiently process long sequences of data while consuming significantly less energy than many conventional AI approaches, the release said on Friday.
According to IIT Guwahati, the researchers from the Mehta Family School of Data Science and Artificial Intelligence at the Institute presented their findings at the prestigious International Conference on Machine Learning (ICML) 2026 in Seoul, South Korea.
The research has significant potential across sectors where continuous data analysis must be performed efficiently. These include wearable health monitoring systems, Internet of Things (IoT) sensors, smart manufacturing, environmental monitoring, autonomous systems, and long-term forecasting applications. In these fields, reducing computational load can extend battery life and enable AI directly on devices without relying heavily on cloud computing.
The model was developed by researchers from SustainAI Lab. Titled ‘Spiking Heterogeneous Harmonic Resonate-and-Fire State Space Model’ (SH²RFSSM), it was presented as a poster at ICML 2026.
The research was co-authored by Kartikay Agrawal, Vaishnavi Nagabhushana, Abhijeet Vikram, Vedant Sharma, and Ayon Borthakur. The work was presented during the ICML 2026 poster session at the COEX Convention and Exhibition Centre in Seoul by Kartikay Agrawal, Vaishnavi Nagabhushana, and Ayon Borthakur on July 7.
Elaborating on the need for such research, Dr. Ayon Borthakur, Assistant Professor, Mehta Family School of Data Science and AI, IIT Guwahati, said, “Modern AI systems increasingly rely on analysing long streams of sequential data such as health signals from wearable devices, environmental sensor readings, industrial monitoring data, and weather or traffic forecasts. However, widely used AI architectures often become computationally expensive as the length of data increases, making them less suitable for battery-powered and resource-constrained devices.”
To address this challenge, the IIT Guwahati team developed SH²RFSSM, a brain-inspired AI architecture that mimics the event-driven communication of biological neurons.
Highlighting the unique aspects of this model, Kartikay Agrawal, PhD Research Scholar, Mehta Family School of Data Science and AI, IIT Guwahati, said, “Unlike conventional neural networks that continuously process information, spiking neural networks activate only when meaningful events occur, enabling sparse and energy-efficient computation. We combined this principle with advanced state space modelling, allowing the system to learn long-range patterns without the heavy computational cost associated with traditional sequence models.”
Speaking about the next steps of this research, Vaishnavi Nagabhushana, PhD Research Scholar, Mehta Family School of Data Science and AI, said, “We aim to further explore the model’s potential across real-world applications that involve continuous and long-range data processing. Our focus will be on improving its efficiency and adaptability so that such AI systems can be deployed more effectively on resource-constrained devices and support practical edge AI applications.”
A distinctive feature of the model is neuronal heterogeneity, where individual artificial neurons are allowed to possess different characteristics rather than behaving identically. This diversity improves the model's ability to capture complex temporal patterns found in real-world sequential data.
The researchers evaluated the AI model across 17 benchmark datasets spanning long-range sequence classification, regression, human activity recognition, and long-term forecasting. The model delivered performance comparable to state-of-the-art sequence models while demonstrating substantially lower estimated energy consumption, making it particularly promising for edge AI applications. (ANI)
IIT Guwahati researchers unveil brain-inspired AI model for energy-efficient long-range data processing
