Unleash the Future: 3 Neuromorphic Computing Architectures That Will Blow Your Mind!

Ever feel like your computer is stuck in the slow lane while your brain is cruising on the information superhighway?

You’re not alone.

For decades, we’ve been trying to force square pegs into round holes with traditional computing, which frankly, just isn’t built for the kind of complex, intuitive tasks that AI demands.

But what if I told you there’s a revolution brewing, one that’s literally taking cues from the most complex, energy-efficient processor known to man: the human brain?

Welcome to the electrifying world of Neuromorphic Computing.

This isn’t just about making faster chips; it’s about fundamentally rethinking how we process information, making our machines not just smarter, but shockingly more efficient and adaptive.

Forget everything you thought you knew about traditional computers. We’re diving deep into an area where silicon starts to behave less like a calculator and more like, well, you!

Get ready, because by the end of this journey, you’ll see why neuromorphic computing isn’t just a buzzword; it’s the next monumental leap in AI, offering solutions that defy the limitations of yesterday.

And trust me, the breakthroughs we’re seeing right now are nothing short of astounding.

The Brain’s Secret Sauce: Why Neuromorphic Computing?

Let’s get real for a moment. Our traditional computers, bless their digital hearts, are built on something called the Von Neumann architecture.

Think of it like this: you have a central processing unit (CPU) and a separate memory unit.

Every single piece of data needs to shuttle back and forth between these two components, over and over again, like a frantic delivery truck on a congested highway.

This constant data movement? It’s called the “Von Neumann bottleneck,” and it’s a massive energy hog and a serious speed bump, especially when we’re trying to tackle complex AI tasks like real-time image recognition or natural language processing.

It’s why your smartphone gets warm when you’re running a fancy AI app, and why massive data centers consume gargantuan amounts of electricity just to train AI models.

Our brains, on the other hand, are masters of efficiency.

Imagine if your memory and your processing power were inextricably linked, happening in the same place at the same time.

That’s essentially how your brain works: neurons (processing units) and synapses (connections and memory) are tightly integrated.

When you recognize a friend’s face or instantly recall a memory, your brain isn’t shuffling data across a bottleneck; it’s performing computations in a massively parallel, distributed, and incredibly energy-efficient way.

It’s not about brute-force calculation; it’s about subtle, adaptive, and highly interconnected processing.

This incredible biological efficiency is the “secret sauce” that neuromorphic computing aims to replicate.

We’re not just trying to make faster versions of old designs; we’re trying to create a completely new paradigm, inspired by nature’s finest supercomputer.

Instead of struggling against the limitations of sequential, clock-driven operations, neuromorphic systems embrace the parallel, event-driven chaos and elegance of biological neural networks.

It’s a fundamental shift, moving from deterministic step-by-step instructions to a world where intelligence emerges from the collective, asynchronous firing of millions of interconnected “neurons.”

And let me tell you, the implications for everything from AI at the very edge of our devices to revolutionary breakthroughs in robotics and sensory processing are nothing short of breathtaking.

This isn’t just incremental improvement; it’s a quantum leap.

The Core Principles: How Neuromorphic Computing Works (and Why it’s Wild!)

So, how exactly do we go from a bunch of silicon and wires to something that acts a bit more like a brain?

It boils down to a few core principles that make neuromorphic chips uniquely powerful and energy-efficient.

It’s not magic, but it certainly feels like it sometimes!

Spiking Neural Networks (SNNs): The Brain’s Language

Forget the traditional Artificial Neural Networks (ANNs) you hear about in deep learning, where data flows in continuous values.

Neuromorphic computing primarily uses Spiking Neural Networks (SNNs).

Imagine neurons in your brain communicating not with a constant hum, but with precise, discrete electrical pulses, or “spikes.”

These spikes are incredibly information-rich, and crucially, they only fire when there’s something significant to transmit.

SNNs mimic this behavior.

Instead of continuously processing all data, neuromorphic neurons only activate and send a spike when their internal “charge” (membrane potential) crosses a certain threshold.

This means computation only happens when it’s absolutely necessary, leading to phenomenal energy savings.

It’s like a highly efficient team that only speaks up when they have critical information, rather than constantly shouting updates.

This event-driven communication is a game-changer.

Event-Driven Processing: Silence is Golden (and Energy Efficient)

Building on SNNs, event-driven processing is a cornerstone of neuromorphic design.

In a traditional computer, everything is synchronized by a global clock.

Every component is constantly working, even if there’s no new data, like an orchestra playing every note in every song, even if only a few instruments are needed for a particular passage.

Neuromorphic chips are different.

They are inherently asynchronous.

Processing only occurs when a “spike” (an event) arrives.

If a neuron isn’t receiving input or isn’t ready to fire, it simply sits there, consuming almost no power.

This “activity-dependent” computation is revolutionary for power efficiency, especially for applications at the “edge” – think smart sensors, drones, or wearable devices that need to operate on minimal battery power for extended periods.

It’s why these chips can be so tiny and still pack a punch.

In-Memory Computing & Co-location: Breaking the Bottleneck

Remember that Von Neumann bottleneck we talked about?

Neuromorphic architectures directly attack it by integrating memory and processing capabilities within the same unit, or at least very close to each other.

This concept is known as “in-memory computing” or “compute-in-memory.”

Instead of data constantly traveling to and from a separate memory bank, the computation happens right where the data is stored, often directly within the “synapses” themselves.

This drastically reduces the energy spent on data movement, which can be far more expensive than the computation itself in traditional systems.

Imagine a chef who has all their ingredients and cooking tools right on their workstation, never having to walk to a separate pantry or supply room.

It makes the entire process incredibly faster and more efficient.

This co-location of memory and computation is perhaps the single most important architectural departure from conventional computers, and it’s what gives neuromorphic systems their astonishing power efficiency.

Massive Parallelism and Adaptive Learning: The Brain’s Symphony

The human brain isn’t just one super-processor; it’s billions of small, relatively simple processors (neurons) working in concert, connected by trillions of synapses.

Neuromorphic chips embody this principle of massive parallelism.

They consist of thousands, even millions, of artificial neurons and synapses, all operating simultaneously and independently.

This distributed processing allows them to handle complex, real-world data streams with incredible speed and robustness.

Furthermore, many neuromorphic architectures support on-chip learning, inspired by the brain’s synaptic plasticity.

This means the connections (synaptic weights) between neurons can change and adapt in real-time based on incoming data, without needing to send information back to a central CPU for training.

This “local learning” capability makes neuromorphic systems incredibly adaptable, allowing them to learn from new experiences continuously, just like our brains do.

It’s not just about executing pre-programmed tasks; it’s about dynamic, continuous evolution.

This combination of event-driven processing, in-memory computation, and massive parallelism makes neuromorphic computing a truly wild frontier in hardware design.

It’s not just an alternative; it’s a vision for a fundamentally more intelligent and efficient future of computing.

Now, let’s peek under the hood at some of the actual chips that are making this vision a reality.

3 Groundbreaking Neuromorphic Hardware Architectures Unveiled

Now that we’ve delved into the “why” and “how” of neuromorphic computing, let’s meet some of the real stars of this show.

These aren’t just theoretical concepts; these are tangible chips, pushing the boundaries of what’s possible in AI hardware.

Each one represents a unique approach to mimicking the brain, with its own strengths and fascinating design philosophies.

Prepare to be impressed by the ingenuity behind these silicon brains!

IBM TrueNorth: The Million-Neuron Marvel

When it comes to neuromorphic pioneering, IBM’s TrueNorth chip stands as a monumental achievement, a true trailblazer in this exciting field.

Unveiled in 2014, TrueNorth was a result of DARPA’s SyNAPSE program, a bold initiative to develop brain-inspired computing architectures.

Imagine a chip with a staggering 1 million programmable neurons and 256 million programmable synapses.

That’s right, a million neurons packed onto a single chip, consuming shockingly little power.

How did they achieve this?

TrueNorth is built on a massively parallel, modular, and event-driven architecture.

It’s composed of 4096 “neurosynaptic cores,” each containing 256 neurons and 65,536 synapses.

These cores communicate asynchronously, meaning they only “wake up” and transmit data when a spike occurs, staying dormant and ultra-low power otherwise.

This “globally asynchronous, locally synchronous” (GALS) design is key to its incredible energy efficiency.

It’s like a vast network of tiny, independent brains, each doing its part only when needed, without a central traffic controller bogging things down.

Unlike traditional CPUs or GPUs, TrueNorth isn’t designed for floating-point calculations; it’s optimized for pattern recognition, associative memory, and real-time sensory processing.

It’s excellent at tasks where data is sparse and event-driven, such as analyzing video streams or processing auditory information.

One of its most impressive feats was running a complex visual object recognition task with unheard-of power efficiency: just 70 milliwatts!

That’s less power than a typical hearing aid.

While TrueNorth primarily focuses on inference (making decisions based on learned patterns) rather than on-chip learning, its sheer scale and power efficiency demonstrated the incredible potential of this architectural shift.

It proved that large-scale brain-inspired hardware wasn’t just a dream, but a tangible reality.

It truly was a “million-neuron marvel” that opened many eyes to the future of computing.

For more fascinating details on IBM’s neuromorphic journey, you might want to check out their research page:

Discover More at IBM Research

Intel Loihi: Learning on the Edge

Hot on the heels of IBM’s foundational work, Intel stepped into the neuromorphic arena with their Loihi research chip, first introduced in 2017, and later refined with Loihi 2.

While TrueNorth focused heavily on scale and inference, Intel’s Loihi brought a significant emphasis on *on-chip learning* – a critical feature if we want truly adaptive AI systems.

Loihi is also designed around spiking neural networks (SNNs) and event-driven processing, but it integrates programmable synaptic learning rules directly into its hardware.

This means the chip can learn and adapt locally, in real-time, without needing to send data back to a traditional CPU or GPU for training updates.

Think of it as the difference between a student who always needs a teacher to correct their homework, versus one who can figure things out and adapt on their own right then and there.

Loihi 1, for instance, packed 128 “neuromorphic cores,” supporting up to 131,072 neurons and 130 million synapses.

Loihi 2, fabricated on Intel’s 4nm process, significantly boosts performance, offering up to 10x faster processing and more complex neuron models, pushing towards 1 million neurons and 120 million synapses per chip.

It’s optimized for solving complex optimization problems, real-time sensing, and dynamic control in scenarios where power is a premium and continuous learning is essential.

Imagine a robot learning to navigate a new environment on the fly, adapting to unexpected obstacles without needing a cloud connection or massive external computation.

That’s the promise of Loihi.

Intel has fostered a vibrant Neuromorphic Research Community (INRC), allowing researchers worldwide to experiment with Loihi and accelerate breakthroughs.

This collaborative approach is vital for pushing the entire field forward, and it highlights Intel’s commitment to making this technology accessible for innovation.

Loihi’s focus on efficient, on-chip learning makes it a powerful contender for edge AI applications, where devices need to be smart, autonomous, and incredibly energy-conscious.

It’s about bringing brain-like intelligence directly to the source of the data.

You can delve deeper into Intel’s vision for neuromorphic computing and the Loihi platform here:

Explore Intel’s Neuromorphic Research

BrainChip Akida: The Ultra-Low-Power Edge AI Master

While Intel and IBM have been making waves, a company named BrainChip has been quietly, yet powerfully, developing their Akida neuromorphic processor, specifically tailored for ultra-low-power edge AI applications.

Akida, meaning “mind” in Greek, lives up to its name by bringing brain-inspired efficiency directly to consumer electronics, industrial IoT, and connected devices where every milliwatt counts.

What sets Akida apart is its laser focus on truly “edge” computing, designed to perform AI tasks with minimal power consumption, often measured in milliwatts, or even microwatts.

Like its counterparts, Akida leverages spiking neural networks and event-based processing.

It’s built to process data only when there’s an actual change or “event,” making it incredibly efficient for always-on sensing and real-time inference.

Imagine a smart speaker that only processes audio when it detects a keyword, or a security camera that only analyzes video when motion occurs, rather than continuously processing all frames.

This event-driven approach isn’t just about saving power; it also reduces the amount of data that needs to be moved around, which, as we discussed, is a huge bottleneck.

Akida’s architecture includes flexible “Akida Neuron Fabric” and supports on-chip learning, allowing devices to adapt and learn from new data directly, without relying on cloud connectivity for every update.

This makes it ideal for applications like local voice recognition, gesture control, sensor fusion, and predictive maintenance right on the device itself.

BrainChip has put a significant emphasis on making Akida developer-friendly, offering tools that integrate with popular machine learning frameworks like TensorFlow, allowing developers to convert their existing AI models to run efficiently on Akida hardware.

This pragmatic approach is crucial for broader adoption of neuromorphic technology.

If you’re looking for AI that can live and learn on your tiny, battery-powered devices, Akida is leading the charge, proving that powerful AI doesn’t need to be a power hog.

It’s reshaping the landscape for pervasive, intelligent edge devices.

For an in-depth look at BrainChip’s Akida and its unique capabilities, their official website is an excellent resource:

Visit BrainChip’s Official Site

Applications That Will Make You Say “Wow”: Where Neuromorphic Shines

Okay, so we’ve talked about the incredible technology, but what can these brain-inspired chips actually *do*?

This is where neuromorphic computing truly moves from fascinating theory to jaw-dropping reality.

Its unique strengths make it perfectly suited for a range of applications where traditional computing struggles with power, latency, or continuous adaptation.

Let’s dive into some scenarios where neuromorphic chips aren’t just an alternative, but often the *only* viable solution.

Edge AI and Smart Sensors: Intelligence at the Source

This is arguably the “sweet spot” for neuromorphic computing.

Imagine a world where every sensor – from your smartwatch to a factory floor monitor – has its own embedded intelligence.

Neuromorphic chips enable ultra-low-power, always-on AI directly on the device, right at the “edge” of the network.

Think about smart home devices that can process voice commands locally without sending everything to the cloud, significantly improving privacy and response time.

Or industrial sensors that can detect anomalies in machinery vibration or temperature in real-time, predicting failures before they happen, all while running on tiny batteries for years.

This isn’t just about convenience; it’s about making our environments truly intelligent and responsive, with reduced network traffic and enhanced data security.

From smart cameras that only record truly important events to health monitors that can continuously analyze your vital signs without draining your battery, the possibilities are endless.

Robotics and Autonomous Systems: Responsive and Adaptive

For robots to truly interact with and understand the world, they need brains that can process sensory input (vision, sound, touch) quickly and efficiently, and then make real-time decisions.

Traditional processors often introduce latency and consume too much power for nimble, autonomous robots.

Neuromorphic chips, with their event-driven nature and in-memory computation, are a perfect fit.

They can enable robots to perceive their environment more like biological beings, reacting instantaneously to changes.

Imagine a drone that can navigate complex, dynamic environments, avoiding collisions and identifying objects on the fly, all while managing its power budget efficiently.

Or humanoid robots that can learn new motor skills and adapt to uneven terrain in real-time, making them far more versatile and capable in unpredictable situations.

This leads to robots that are not only more energy-efficient but also more robust, adaptable, and genuinely intelligent in their interactions with the physical world.

Medical Devices and Healthcare: Personalized and Proactive

The human brain is the ultimate biological computer, so it’s only natural that brain-inspired chips find applications in healthcare.

Imagine smart prosthetics that can interpret neural signals more accurately and adapt to a user’s movements with unprecedented fluidity, providing a more natural and responsive experience.

Or intelligent implants that can monitor neurological activity, detecting the onset of seizures or other conditions and providing real-time feedback or intervention.

Neuromorphic chips’ low power consumption and ability to process complex, noisy biological signals make them ideal for wearable and implantable medical devices.

They could lead to more personalized treatments, proactive health monitoring, and a deeper understanding of neurological disorders, potentially revolutionizing how we approach chronic diseases and rehabilitation.

Big Data Analytics and Pattern Recognition: Finding Needles in Haystacks

While often highlighted for their edge capabilities, neuromorphic chips also hold immense promise for large-scale data analytics, particularly in areas requiring complex pattern recognition across vast datasets.

Their ability to process sparse, event-driven data makes them incredibly efficient for tasks like anomaly detection in financial transactions, cybersecurity threat detection, or even scientific discovery.

Imagine sifting through petabytes of network traffic to identify unusual patterns indicative of a cyberattack, not by brute-force searching, but by recognizing the “signature” of malicious activity in real-time.

Or analyzing astronomical data to spot faint, unexpected signals that conventional methods might miss.

Neuromorphic systems can excel where traditional methods struggle with the sheer volume and complexity of data, offering a pathway to uncover hidden insights with remarkable energy efficiency, even in massive data centers.

They are not just about small devices; they are about smarter ways to handle big information challenges.

The Road Less Traveled: Challenges and Hurdles

While the future of neuromorphic computing shines bright, it’s only fair to acknowledge that this revolutionary technology isn’t without its speed bumps and uphill climbs.

Innovation rarely happens without a few tough challenges, and neuromorphic computing is definitely on the road less traveled!

Programming Complexity: A New Language for a New Brain

One of the biggest hurdles right now is the programming paradigm itself.

We’ve spent decades perfecting software for the Von Neumann architecture, with its sequential instructions and well-understood memory access patterns.

Neuromorphic chips, with their asynchronous, event-driven nature and massively parallel processing, require a fundamentally different way of thinking and programming.

Developing algorithms for spiking neural networks (SNNs) and mapping them efficiently onto these unique hardware architectures is a complex task.

It’s like learning a whole new language, one that requires a deep understanding of neuroscience principles and novel computational models.

The tooling and software frameworks are still maturing, which means there’s a steep learning curve for developers used to conventional AI programming.

This lack of widespread, easy-to-use software development kits (SDKs) and standardized programming models can slow down adoption and innovation.

It’s not impossible, just a new frontier for software engineers to conquer!

Lack of Standardization: The Wild West of Hardware

Unlike traditional CPUs and GPUs, where architectures are largely standardized (think x86 or ARM), the neuromorphic landscape is currently a “Wild West” of diverse designs.

Each research group and company is exploring its own unique approach to neuron models, synapse implementation, and inter-chip communication.

While this fosters incredible innovation, it also creates compatibility challenges.

It’s difficult to compare performance across different platforms, and porting algorithms from one neuromorphic chip to another can be a nightmare.

This lack of standardization hinders widespread adoption and the creation of a robust ecosystem.

For neuromorphic computing to truly take off, there will likely need to be some convergence or at least clearer interoperability standards, much like how various GPU manufacturers eventually settled on common programming interfaces.

Benchmarking and Metrics: How Do We Even Compare Them?

Following on from standardization, how do you even measure the “performance” of a neuromorphic chip?

Traditional metrics like FLOPS (floating point operations per second) or TOPS (tera operations per second) don’t fully capture the unique advantages of event-driven, energy-efficient processing.

A neuromorphic chip might perform fewer raw “operations” but achieve the same or better results for a specific AI task with vastly less power.

Developing new, relevant benchmarks and metrics that accurately reflect the strengths of neuromorphic architectures – focusing on things like energy-delay product, spike throughput, or learning efficiency per watt – is a critical, ongoing challenge.

We need a common language to properly evaluate and compare these innovative systems.

Integration with Existing Infrastructure: A Hybrid Future?

For all its promise, neuromorphic computing isn’t going to replace traditional CPUs and GPUs overnight, nor should it.

They excel at different types of tasks.

The challenge lies in integrating these specialized accelerators seamlessly into existing computing infrastructure.

How do they communicate with conventional processors? How do you manage heterogeneous systems that combine traditional and neuromorphic hardware?

The most likely path forward involves hybrid systems, where neuromorphic chips handle specific, brain-like tasks (like real-time sensing or continuous learning at the edge), while traditional processors manage the more symbolic, sequential computations.

Building effective software and hardware interfaces for these hybrid systems is a complex engineering feat, but it’s essential for maximizing the benefits of both worlds.

Overcoming these challenges will require continued dedication from researchers, engineers, and industry leaders, but the potential rewards are so immense that the journey is undoubtedly worth it.

The future isn’t just about faster calculations; it’s about smarter, more energy-conscious, and truly adaptive intelligence.

The Dazzling Future: What’s Next for Neuromorphic Computing?

So, where do we go from here?

The challenges are real, but the potential of neuromorphic computing is simply too vast to ignore.

As we overcome these hurdles, the future of AI and computing is poised to become incredibly exciting, pushing boundaries we can only begin to imagine.

Let’s gaze into the crystal ball and see what’s on the horizon for these brain-inspired marvels.

Ubiquitous Edge Intelligence: AI Everywhere, All the Time

We’re already seeing hints of this, but in the future, neuromorphic chips will make “AI at the edge” not just a niche application, but a pervasive reality.

Imagine every device around you – from your smart glasses to your refrigerator, from traffic lights to agricultural sensors – imbued with sophisticated, always-on intelligence, consuming minimal power.

These devices will not only perceive and react to their environment in real-time but also learn and adapt continuously, offering personalized and proactive assistance without constant cloud connectivity.

This means enhanced privacy, incredible responsiveness, and a truly smart environment that anticipates your needs.

The era of “ambient intelligence” where AI fades into the background, seamlessly enhancing our lives, is on the horizon, powered by neuromorphic computing.

Toward Artificial General Intelligence (AGI): A More Human-like Path?

This is the holy grail of AI research: creating systems that can understand, learn, and apply intelligence across a broad range of tasks, much like humans do.

While traditional deep learning has made incredible strides in narrow AI, neuromorphic computing offers a biologically more plausible pathway toward AGI.

By mimicking the brain’s fundamental principles of distributed processing, event-driven computation, and continuous, unsupervised learning, neuromorphic systems could unlock new approaches to common-sense reasoning, lifelong learning, and perhaps even consciousness.

It’s not a guarantee, but many believe that by building machines that process information more like our brains, we might just stumble upon the foundational elements needed for true general intelligence.

It’s a long shot, but an incredibly intriguing one, and neuromorphic architectures are a key part of that exploration.

New Materials and Devices: Beyond Silicon

The current generation of neuromorphic chips largely relies on traditional CMOS (Complementary Metal-Oxide-Semiconductor) technology, albeit with innovative architectures.

However, the future is likely to see the integration of entirely new materials and devices that can more closely mimic biological synapses and neurons.

Think about memristors, phase-change memory (PCM), or even spintronic devices.

These emerging non-volatile memory technologies hold the promise of even denser, more energy-efficient, and truly analogue synaptic weights, allowing for higher degrees of plasticity and continuous learning directly within the memory itself.

This “beyond Von Neumann” exploration into novel materials could further reduce power consumption and increase the computational power per unit area, making neuromorphic chips even more potent.

We’re literally building brains out of new elements!

Hybrid Systems and Co-Design: The Best of All Worlds

As mentioned earlier, the future isn’t about one technology replacing another, but rather about synergistic integration.

We’ll see more sophisticated hybrid systems where neuromorphic processors work in concert with traditional CPUs, powerful GPUs, and even quantum computers.

This “co-design” approach will allow each specialized processor to do what it does best:

Neuromorphic chips handling real-time, event-driven pattern recognition;

GPUs accelerating massive parallel tensor operations for deep learning;

CPUs managing symbolic logic and control;

and perhaps quantum computers tackling intractable optimization problems.

The true power will lie in designing seamless software and hardware interfaces that allow these diverse computing paradigms to collaborate, creating systems far more powerful and versatile than any single architecture could achieve alone.

It’s a symphony of computing, with neuromorphic chips playing a critical, unique instrument.

Wrapping it Up: The Neuromorphic Revolution is Here

We’ve embarked on a fascinating journey through the world of neuromorphic computing, a field that’s not just tweaking existing technology but truly reinventing the wheel, inspired by the ultimate biological masterpiece – the human brain.

From breaking the age-old Von Neumann bottleneck to enabling incredibly energy-efficient, event-driven processing with spiking neural networks, neuromorphic hardware is poised to transform AI as we know it.

We’ve explored the groundbreaking efforts of pioneers like IBM with their massive TrueNorth chip, Intel with its on-chip learning Loihi platform, and BrainChip with its ultra-low-power Akida, each demonstrating unique strengths and pushing the boundaries of what’s possible.

These chips aren’t just for sci-fi movies anymore; they’re enabling real-world “wow” applications in edge AI, robotics, healthcare, and big data analytics, offering solutions that were once considered impossible due to power or latency constraints.

Of course, the road ahead isn’t entirely smooth.

Challenges like programming complexity, the need for standardization, and developing new performance benchmarks still need to be addressed.

But the sheer potential for ubiquitous, energy-efficient, and truly adaptive intelligence is a powerful motivator.

The future promises a world where AI is seamlessly integrated into our daily lives, where devices are not just smart but truly intelligent, learning and adapting continuously with minimal power.

This isn’t just about faster computers; it’s about fundamentally rethinking intelligence, and that, my friends, is a revolution worth watching.

Neuromorphic computing is here, and it’s set to redefine our digital future.

Ready to jump in?

Neuromorphic Computing, Brain-Inspired AI, Spiking Neural Networks, Edge AI, Energy Efficiency