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:
âEDGE-X 2025: Reimagining edge intelligence with low-power, high-efficiency AI systemsâ.
đ⨠Rethinking Edge Intelligence at EDGE-X 2025 đ Co-located with AI-ML Systems Conference 2025 | Chancery Pavilion, Bangalore | October 8, 2025 As intelligent systems scale across diverse environmentsâfrom IoT sensors to autonomous platformsâtraditional architectures face new limits. The EDGE-X...
www.linkedin.com
View attachment 87849
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 environmentsâfrom IoT sensors to autonomous devicesâtraditional 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We solicit submissions describing original and unpublished results focussed on leveraging software agents for software engineering tasks.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
- TinyML, binary/ternary neural networks, federated learning
- Model pruning, compression, quantization, and edge-training
2.Hardware-Software Co-Design
- RISC-V custom extensions for edge AI
- Non-von-Neumann accelerators (e.g., in-memory compute, FPGAs
3.Beyond CMOS & von Neumann Paradigms
- 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
4.System-Level Innovations
- Near-/sub-threshold computing
- Power-aware OS/runtime frameworks
- Approximate computing for error-tolerant workloads
5.Tools & Methodologies
- Simulators for emerging edge devices (photonic, spintronic)
- Energy-accuracy trade-off optimization
- Benchmarks for edge heterogeneous platforms
6.Use Cases & Deployment Challenges
- 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