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Arijit Mukherjee is already busy co-organising another Edge AI workshop that will also touch on neuromorphic computing. It is scheduled for 8 October and will be co-located with AIMLSys 2025 in Bangalore:
“EDGE-X 2025: Reimagining edge intelligence with low-power, high-efficiency AI systems”.
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🔋✨ Rethinking Edge Intelligence at EDGE-X 2025 | Arijit Mukherjee
🔋✨ Rethinking Edge Intelligence at EDGE-X 2025 📍 Co-located with AI-ML Systems Conference 2025 | Chancery Pavilion, Bangalore | October 8, 2025 As intelligent systems scale across diverse environments—from IoT sensors to autonomous platforms—traditional architectures face new limits. The EDGE-X...www.linkedin.com
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Workshop-EDGE-X | The Fifth International Conference on AI ML Systems
www.aimlsystems.org
EDGE-X
The EDGE-X 2025 workshop, part of the Fifth International AI-ML Systems Conference (AIMLSys 2025), aims to address the critical challenges and opportunities in nextgeneration edge computing. As intelligent systems expand into diverse environments—from IoT sensors to autonomous devices—traditional applications, architectures, and methodologies face new limits. EDGE-X explores innovative solutions across various domains, including on-device learning and inferencing, ML/DL optimization approaches to achieve efficiency in memory/latency/power, hardware-software co-optimization, and emerging beyond von Neumann paradigms including but not limited to neuromorphic, in-memory, photonic, and spintronic computing. The workshop seeks to unite researchers, engineers, and architects to share ideas and breakthroughs in devices, architectures, algorithms, tools and methodologies that redefine performance and efficiency for edge computing.
Topics of Interest (including but not limited to the following):
We solicit submissions describing original and unpublished results focussed on leveraging software agents for software engineering tasks. Topics of interest include but are not limited to:
1.Ultra-Efficient Machine Learning
2.Hardware-Software Co-Design
- TinyML, binary/ternary neural networks, federated learning
- Model pruning, compression, quantization, and edge-training
3.Beyond CMOS & von Neumann Paradigms
- RISC-V custom extensions for edge AI
- Non-von-Neumann accelerators (e.g., in-memory compute, FPGAs
4.System-Level Innovations
- Neuromorphic computing (spiking networks, event-based sensing)
- In-memory/compute architectures (memristors, ReRAM)
- Photonic integrated circuits for low-power signal processing
- Spintronic logic/memory and quantum-inspired devices
5.Tools & Methodologies
- Near-/sub-threshold computing
- Power-aware OS/runtime frameworks
- Approximate computing for error-tolerant workloads
6.Use Cases & Deployment Challenges
- Simulators for emerging edge devices (photonic, spintronic)
- Energy-accuracy trade-off optimization
- Benchmarks for edge heterogeneous platforms
- Self-powered/swarm systems, ruggedized edge AI
- Privacy/security for distributed intelligence
- Sustainability and lifecycle management
- Program Committee
- Arijit Mukherjee, Principal Scientist, TCS Research
- Udayan Ganguly, Professor, IIT Bombay
… make that two workshops Arijit Mukherjee is currently co-organising!
PReMI 2025 Workshop NCEI
Welcome to the OpenReview homepage for PReMI 2025 Workshop NCEI
The 11 December NCEI (“Neuromorphic Computing for Edge Intelligence”) 2025 workshop will be co-located with the 11th International Conference on Pattern Recognition and Machine Intelligence (PReMI'25) at IIT Delhi.
One of Arijit Mukherjee’s three co-organisers is Manan Suri, who is a professor at IIT Delhi and founded CYRAN AI Solutions as a spinoff from his uni lab in 2018. He has also been a Research Advisor to the TCS Innovation Team on Neuromorphic Computing and Edge AI since June 2021.
(cf. https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-458643)
Another co-organiser is Mukherjee’s TCS Research colleague Sounak Dey.
“With AI’s rapid advancement, integrating AI into edge devices has become crucial for real-time, low-latency, and privacy-sensitive applications. Edge computing processes data near its source, while neuromorphic computing, inspired by the human brain, follows non von Neumann paradigm of computing and offers extremely power efficient edge solutions. The PReMI 2025 workshop on Neuromorphic Computing for Edge Intelligence aims to unite researchers, practitioners, and enthusiasts to explore energy-efficient signal analysis at the edge. By merging edge computing’s decentralized approach with neuromorphic systems, we can develop innovative solutions addressing scalability, energy efficiency, and adaptability challenges in AI edge deployment.”