Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Design and train custom spiking neural networks (SNNs) to unlock ultra-low-power, real-time AI at the edge.
Traditional deep learning is power-hungry and struggles with temporal data. Spiking Neural Networks (SNNs) mimic biological neurons, using sparse, event-driven computation to achieve >10x energy efficiency on neuromorphic hardware like Intel Loihi or BrainChip Akida. We build SNNs that process continuous sensor streams with millisecond latency on microwatts of power.
Deploy always-on intelligence for battery-powered devices, from industrial sensors to autonomous drones.
Our development process delivers:
Nengo and Lava for your specific temporal signal processing task.Move beyond proof-of-concept. We provide the engineering rigor to transition from research papers to field-deployed systems. Explore our related service for full-stack implementation: Neuromorphic AI Edge Deployment or learn about our strategic Neuromorphic System Architecture Consulting.
Our custom SNN development translates the theoretical efficiency of neuromorphic computing into measurable business advantages, from slashing operational costs to enabling new product categories.
Deploy AI inference at the edge with power consumption measured in milliwatts, not watts. Our SNN designs leverage the sparse, event-driven computation of neuromorphic processors like Intel Loihi to enable battery-powered devices that operate for years, not days.
Achieve sub-millisecond, predictable latency for time-critical applications. Unlike traditional deep neural networks with variable batch processing, our spiking neural networks process sensory events as they occur, essential for robotics, industrial control, and high-frequency signal analysis.
Move complex pattern recognition and sensory fusion directly to the endpoint. By performing intelligent processing locally with SNNs, you eliminate constant bandwidth costs, reduce latency, and enhance data privacy—critical for applications in remote industrial sites or consumer devices.
Accelerate from concept to deployed system with our team's deep experience in neuromorphic software-hardware co-design. We handle the full stack—from SNN model design and training to deployment and performance tuning on target hardware—ensuring a production-ready outcome.
A phased breakdown of a standard spiking neural network development engagement with Inference Systems, outlining key deliverables and timeframes for each stage.
| Project Phase | Key Activities | Typical Duration | Primary Deliverables |
|---|---|---|---|
Phase 1: Discovery & Architecture | Requirements analysis, neuromorphic hardware selection (Loihi, Akida), SNN topology design | 1-2 weeks | Technical specification document, architecture diagram, project roadmap |
Phase 2: Model Development & Simulation | SNN design in Nengo/Lava, training with surrogate gradients, simulation-based validation | 3-5 weeks | Trained SNN model, simulation performance report, energy efficiency projections |
Phase 3: Hardware Deployment & Optimization | Porting to target neuromorphic chip, latency/power optimization, quantization | 2-3 weeks | Optimized model binary, deployment scripts, baseline performance benchmarks |
Phase 4: Integration & Testing | API/service wrapper development, integration with client systems, real-world validation | 2-4 weeks | Integrated prototype, test suite results, operational documentation |
Phase 5: Production Readiness | Performance tuning, security review, deployment automation, monitoring setup | 1-2 weeks | Production-ready deployment package, SLA documentation, monitoring dashboard |
Total Project Timeline | 9-16 weeks | Fully functional, optimized SNN system deployed on neuromorphic hardware |
Our Spiking Neural Network development delivers tangible business outcomes by leveraging the temporal dynamics and sparse computation of neuromorphic hardware. We build systems that process real-world signals with millisecond latency and microwatt power consumption, enabling new product categories and operational efficiencies.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers to the most common questions about our process, timelines, and outcomes for custom spiking neural network development.
From initial design to production-ready deployment, a typical project takes 6-10 weeks. This includes 2 weeks for architectural design and dataset preparation, 3-5 weeks for model development and training using frameworks like Nengo or Lava, and 1-3 weeks for optimization and integration onto target neuromorphic hardware (e.g., Intel Loihi, BrainChip Akida). Complex multi-sensor fusion projects may extend to 14 weeks.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
