An engineer at Microsoft already has noticed Brainchip’s AKIDA:. The whole article is interesting but I have only extracted the part covering Neuromorphic Computing

Neuromorphic Computing: Building Brain-Inspired, Energy-Efficient AI

As the computational and energy demands of large-scale AI — particularly the colossal LLMs and the resource-hungry self-improving systems — continue their meteoric rise, they cast a looming shadow: an undeniable bottleneck. This pressing challenge is precisely what Neuromorphic Computing steps forward to address, representing nothing less than a fundamental paradigm shift in how we design and build AI hardware. Drawing profound inspiration from the astonishing energy efficiency and parallel processing power of the human brain, neuromorphic chips bravely jettison the traditional von Neumann architecture, which, for decades, has inefficiently separated processing from memory, leading to constant, energy-intensive data movement.

Key principles defining this quiet revolution in silicon include:

• In-Memory Computing (Processing-in-Memory): In stark contrast to conventional architectures, neuromorphic systems ingeniously co-locate processing units directly within or immediately adjacent to memory. This radical approach dramatically curtails the energy consumption associated with constantly shuttling data between distinct processing and storage components — the infamous “von Neumann bottleneck.” This architecture fundamentally mirrors the brain’s seamless, integrated computation and memory, operating with a fluidity unmatched by current digital systems.
• Event-Driven (Spiking Neural Networks — SNNs): Unlike typical deep learning models that process all inputs continuously, consuming power constantly, neuromorphic chips primarily operate on Spiking Neural Networks (SNNs). These artificial neurons “fire” (generate a computational event) only when a certain threshold of input is reached, mimicking the sparse, asynchronous, and incredibly efficient communication of biological neurons. This event-driven processing leads to extraordinarily low power consumption, as computations are performed only when genuinely necessary, minimizing idle energy drain. Imagine a light switch that only consumes power when it’s actively flipping.
• Intrinsic Parallelism and On-Chip Adaptability: Neuromorphic architectures are inherently massively parallel, allowing for millions of concurrent computations, much like the brain’s distributed processing. Furthermore, many neuromorphic designs are built for continuous, on-device learning and adaptation, making them uniquely suited for dynamic, real-world edge environments where constant cloud connectivity is impractical or impossible.

The critical and rapidly escalating role of neuromorphic computing in 2025 cannot be overstated:

• Addressing the Energy Crisis of AI: The monumental carbon footprint and staggering operational costs associated with training and running today’s colossal AI models are simply unsustainable. Neuromorphic chips offer a revolutionary path to orders of magnitude lower power consumption for demanding AI tasks, making large-scale AI deployment far more environmentally responsible and economically viable. This isn’t just an optimization; it’s an existential necessity for AI’s long-term, widespread scalability.
• Fueling the Edge AI Revolution: By enabling sophisticated AI to run directly on tiny, power-constrained devices — from next-generation wearables and smart sensors to agile drones and truly autonomous robotics — neuromorphic chips unleash the full potential of real-time, on-device intelligence. This dramatically reduces latency, enhances data privacy (as less sensitive data needs to be transmitted to the cloud), and facilitates always-on AI capabilities crucial for applications where consistent cloud connectivity isn’t feasible or desirable. Picture smart eyewear that provides real-time contextual awareness without draining its battery in minutes, or a drone performing complex environmental analysis on its own, far from any network.
• Opening New Frontiers in AI Application: This unprecedented energy efficiency and real-time processing ability enable novel AI applications that were previously confined to laboratories or supercomputers due to power constraints. Consider medical implants with embedded AI that continuously monitor biomarkers and adapt their function for years without external power, or vast smart city sensor networks that process complex visual and auditory data locally to manage traffic or detect anomalies without overwhelming central servers.

Leading the charge in this hardware revolution are innovators like Intel, with its groundbreaking Loihi series. Loihi 2, in particular, is pushing the boundaries of AI with its support for low-precision, event-driven computation, showing promising results for efficient LLM inference, demonstrating capabilities like real-time gesture recognition and pattern learning with vastly reduced power requirements. (Loihi 2 and its capabilities). IBM also continues its advancements in neuromorphic computing, with ongoing research pushing the boundaries of brain-inspired architectures. Meanwhile, companies like Brainchip are commercializing their Akida chip, a fully digital, event-based AI processor ideal for ultra-low power edge computing, demonstrating advanced capabilities in areas like event-based vision for autonomous vehicles and industrial automation. (See how Brainchip’s Akida is enabling breakthroughs in edge AI.). As these specialized processors mature and become more widely accessible, they promise to fundamentally reshape the hardware landscape of AI, driving us towards a future where intelligence is not just powerful, but also profoundly efficient, always-on, and truly pervasive.

Written by Adit Sheth

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Senior Software Engineer at Microsoft, I lead advancements in Copilot, LLMs, and AI, focusing on prompt engineering to reshape intelligent systems

https://pub.towardsai.net/unlocking-ais-next-wave-how-self-improving-systems-neuromorphic-chips-and-scientific-ai-are-fe97783392c1

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