Pasteur’s Magic Quadrant in AI: The Fusion of Fundamental Research and Practical

In the world of scientific research, few figures embody the fusion of theory and application like Louis Pasteur. His legacy inspired Pasteur’s Quadrant, a framework Donald E. Stokes defined in his 1997 book Pasteur’s Quadrant: Basic Science and Technological Innovation. Stokes showed that the most transformative discoveries arise when research pursues both fundamental understanding and practical impact. 

Some research is pursued for pure mental enjoyment or curiosity. Group theory was created in a single night in 1832 by 20-year-old Évariste Galois scribbled in desperation before he faced a fatal duel over a romantic rival. Two hundred years later, it powers the cryptographic algorithms securing global commerce. 

In business, research must generate outcomes. But when we fuse deep theory with those outcomes, the impact becomes outsized. 

No one illustrates this better than Pasteur. His drive to understand microorganisms produced germ theory of disease and with it, vaccines and pasteurization. These discoveries turned invisible microbes into the root cause of disease, birthing modern medicine and hygiene. They have saved hundreds of millions of lives and remain the foundation of global health systems today. This is the power of theory and application working in unison. 

At BrainChip, this philosophy lives in our Research and Development team. We are driven by curiosity. We are guided by purpose. 

Our neuromorphic work begins with the brain’s sparse computation. Only about 1% of neurons in the brain are active at any time. BrainChip’s Temporal Event-based Neural Network (TENNs) model introduces a second biological principle: system state. State is a compact representation of everything the network has seen since activation. State must be useful for tasks, easy to compute, and simple to maintain. Traditional AI largely ignores it. Our scientists solved the puzzle of how to make state and to yield enormous benefits. 

These two ideas, sparsity and state, form the theoretical foundation of everything we do at BrainChip. 

We didn’t wait two centuries for theory to pay off. Below, I share the breakthroughs our team delivered in the summer of 2025. I hope you find them as exciting as I do. 

 

Turning Theory into Tangible Progress

During the summer of 2025, our Research and Development team achieved several milestones that advanced both the science and business of edge AI. Working at the intersection of hardware and algorithms, we’ve made strides that will shape the future of BrainChip’s Akida neuromorphic platforms and our customers’ success across industries.

 

Radar and Electronic Warfare: Real-World Defense Applications 

We developed and deployed advanced machine learning models for radar and electronic warfare. One key achievement was denoising Synthetic Aperture Radar (SAR) images using Akida 1500 and 2.x hardware. We discovered a method that dramatically reduces the power and latency of the system by orders of magnitude.  

We also applied our newest TENN algorithms to classify radar signals, achieving state of the art accuracy, again with orders of magnitude less computation. This algorithm will run on our newest generation of hardware now in development. 

Our innovations in radar could help bring complex sensors to lightweight aircraft.  It might be part of systems that provides early warning and can confuse hostile actors. 

 

Generative AI at the Edge: Efficiency Meets Creativity 

We’re also bringing the power of Generative AI closer to where data lives. Through innovative quantization techniques, our team achieved near-identical model performance at 4-bit levels versus 32-bit floating point, reducing memory and bandwidth by a remarkable factor of 8 times.  This innovative compression technique is patent pending, and its enablement will be baked into new Akida hardware. 

Our novel technique uses powerful optimization and a quantizing scheme for each model. These breakthroughs make it possible to run large language models (LLMs), speech recognition, and text-to-speech on low-power devices without sacrificing quality. 

We were also successful in implementing our PreCog algorithm which dramatically speeds up retrieval in Retrieval Augmented Generation (RAG) systems, a ubiquitous method for reducing hallucinations and improving accuracy in LLMs.

 

Transformers vs. State-Space Models: Shaping the Next Generation

We built a comprehensive simulation framework comparing Transformer and State-Space Model (SSM) architectures that provide critical insights into memory, bandwidth, and power trade-offs. These results directly inform future hardware co-design paving the way for high-performance, low-energy accelerators that bring LLMs and advanced audio processing to the edge.

 

Anomaly Detection and Health Monitoring: Always-On Intelligence

Our work with SSMs also opened new frontiers in time-series anomaly detection for industrial IoT and healthcare. Compact, efficient models can now detect subtle mechanical faults or cardiac irregularities in real time delivering actionable insights where and when they matter most. Our algorithms will run on Akida Pico, a sub milliwatt computing device.

 

Investing in the Future: People and Potential

Innovation at BrainChip is powered by people. Our internship program embodies the spirit of Pasteur’s Quadrant bridging theory and practice. This summer, interns contributed breakthroughs in fall detection, large-image segmentation, RF signal classification, and power estimation tools, strengthening our foundation for applied neuromorphic research. 

For future employees, this is your invitation: join a team where fundamental science meets real-world impact where ideas evolve into technologies that reshape industries. For customers and investors, it’s proof that our innovation is not just theoretical; it’s operational, delivering measurable results today while paving the path for tomorrow’s breakthroughs.

 

Looking Ahead

At BrainChip, we live in Pasteur’s Quadrant where science meets purpose and innovation meets impact. 

As hardware and algorithms continue to co-evolve, BrainChip’s Applied Research team remains committed to advancing low-power AI that learns and acts like the brain itself. From defense to healthcare, from the factory floor to autonomous systems, we’re building technologies that make intelligence ubiquitous, efficient, and accessible, one breakthrough at a time.

Author

Dr. Tony Lewis is the Chief Technology Officer at BrainChip specializing in brain-inspired AI and robotics. Tony was Global Head of the Artificial Intelligence and Emerging Compute Lab at HP, Inc. At Qualcomm, Inc,Tony headed Neuromorphic, Deep Learning and Robotics efforts. Tony has held faculty positions or held leadership positions at UCLA, UIUC, and the Univ. of Arizona. Tony holds a Doctor of Philosophy degree in Electrical Engineering from University of Southern California and a Bachelor of Science in Cybernetics/Applied Math from UCLA.