The case for Neuromorphic Computing

Introduction

Neuromorphic computing is an innovative approach to computing that emulates the structure and function of the human brain using specialized hardware and software. It leverages spiking neural networks (SNNs) to process information through events or spikes, offering energy-efficient, real-time computation. By mimicking biological neurons and synapses, neuromorphic systems excel in adaptive learning, sensory processing, and decision-making tasks. This is a paradigm shift in term of Digital Computation based on the Processor and Memory models based on logic that is binary based (as in Von Neumann model).

Neuromorphic computing, offers advantages for AI and Multimodal sensing since by definition it mimics biological neural networks. This allows for low-power, and real time data processing.

History

The following chart provides perspective into the history of Neuromorphic computation.

The table below provides an insight into the different forms of computing.

The segue to Neuromorphic architecture requires both HW and SW changes viz.,

Neuromorphic Hardware :

1. Relies on asynchronous communication (for scaling and speed)
2. Does away with clock driven architectures that saves energy
3. Scaling is done via more hardware tiling rather than increasing clock frequency thereby reducing power consumption
4. Uses spikes (for power efficiency)
5. Very parallel, as scaling is done by means of adding more units (cores)
6. Target low-power
7. Adaptively Learn
8. More Accurate, Faster, Lower Power

Neuromorphic Software

1. Need Algorithms to match the Hardware.
2. Traditional AI Algorithms like RNN, LSTM etc do not work best for Neuromorphic compute therefore new SW algorithms are required
3. The following table gives an quick overview of some of the Key Neuromorphic algorithms

Benefits in AI & Sensing

Energy Efficiency

• The event driven nature of neuromorphic computing allows only activation based on input data, and therefore reducing idle energy usage.
• The analogous nature of inputs and weights, makes multiplications easier and more energy efficient.

Superior Scaling

• Enables addition of additional tiles with cores for scaling to greater performance and workloads with minimal impact on power consumption, unlike traditional computing which requires increasing of clock frequency and thereby increasing power consumption

Real Time Processing

• Neuromorphic processors handle sensory data efficiently by processing it locally in analogous format, good for latency critical applications
• Greater accuracy - Enable greater accuracy at similar power consumption due to adaptive learning benefits
• Adaptive Learning - Neuromorphic computing by design (Plasticity) is enabled for adaptive learning

Spike Based Neural Networks

Using spiking neural networks (SNNs), neuromorphic chips process data similar to biological forms in analogous form making it easier for certain tasks, decision-making and complex sensory data analysis.

Segments

Neuromorphic is well suited for the following segments.

• Edge AI - are typically fitted wtih multiple forms of sensing, and have RT requirements with low latencies
• Automotive - require latency critical handling from multiple sensors viz, LiDAR, RADAR, Vision. Neuromorphics chips can process this effectively
• Wearables and Consumer Devices - Wearables and health monitors benefit greatly both from the natural signal processing in analogous format, and the power efficiencies provided
• Mobile Devices - ideal for voice, vision and proximity, and gesture sensing.

Use Cases

The following use cases are well enabled by Neuromorphic computing:

Pattern Recognition - Voice analysis, HR/ECG/EEG analysis, Image recognition

Autonomous Systems - Self driving cars, drones, Robotics,

Smart Sensors - IOT Devices

Health Monitoring - EEG/EEG/HR

Gesture Recognition -Advanced Gestures recognition

Challenges and Opportunities

Technological Immaturity - nascent SW ecosystem, programming models and tools

Limited Apps: nice areas like RT and low power tasks

High R&D costs: R&D, prototyping and manufacturing

Early stages of investor funding

Expected Maturity Timeframe

Neuromorphic computing is still in nascent stages, commercial viability might happened over the next 3-5 years, early applications are already emerging in specific segments.

Edge AI ; As early as 2025

AI Acceleration : Supplemental and Replacement systems around end of this decade

Autonomous Systems: Early next decade

Companies

The following Companies are invested in Neuromorphic Computing :

Intel, IBM, BrainChip, Synsense, GrAI Matter, Innatera, BrainLabs, Prophesee, Standford University, SpinNaker

The Future

Neurmorphic Computing promises the existing digital computing paradigm, and promises to be successful for the following reasons:

Energy Efficiency- with the advent of AI, and the enormous power consumption challenges that AI in its current predominant GPU/CPU architecture brings, needs alternatives despite Cerebras and Groq bringing in advances with memory access and its benefits

Real-Time Processing and Adaptability - In particular for sensing (Vision, Sound, Motion, EEG/ECG Waveforms), Gestures making them ideal for Automotive, IOT, Edge, Wearable and Heath segments. They are inherently adaptive, learning from experience with less need for extensive retraining.

Brain like Intelligence evolution: Neuromorphic chips could enable systems via Spike Neural Networks and unsupervised learning. Their capacity to integrate sensing and learning at the hardware makes them more efficient.