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February 2025
By: The Non-Von Team The rise of artificial intelligence (AI) is disrupting up industries, fuelling innovation, and changing the way organizations handle data. At the heart of this transformation are AI chips—specialized processors built to tackle the heavy lifting of modern AI applications. However, many organizations are still relying on traditional processors for their AI needs, and that’s a choice with risks. From edge computing failures to data bottlenecks, inefficient operations, and even power outages from overtaxed hardware, sticking with outdated processors is becoming more problematic every day. 1. Failed Edge Computing: A Roadblock to Real-Time Decisions Edge computing brings data processing closer to the source—whether that’s an autonomous vehicle, an industrial machine, or an IoT device. This setup enables quick decision-making right where the data is being generated, without relying on distant servers. However, traditional CPUs and GPUs often fall short when it comes to the low-latency and power efficiency that edge applications demand. Without AI chips, systems can face: ● Higher latency: Traditional processors struggle with the parallel processing needed for real-time tasks, causing delays in critical decisions (Google + Deloitte Impact Report). ● Increased failure rates: Many edge devices can’t handle continuous computation on older processors, leading to downtime during important operations (Google + Deloitte Impact Report). These limitations show just how outdated hardware can hold back edge devices, potentially causing costly errors or system failures. Upgrading to AI chips ensures the real-time performance needed for those critical edge applications to run smoothly. 2. Stranded Data: The Untapped Resource With traditional processors, the sheer volume of data generated in modern systems often overwhelms the hardware’s ability to process it. This leads to stranded data—valuable information that remains unanalysed due to insufficient computational capacity. ● Data Generation Outside Traditional Data Centers: Analysts predict that by 2025, a significant portion of enterprise-generated data will be created and processed outside traditional centralized data centers or clouds. This shift underscores the growing importance of edge computing in handling the increasing volume of data generated by enterprises (Comarch) ● Impact on Industries Like Healthcare: The healthcare sector, among others, is expected to benefit from real-time data analytics enabled by advanced processing capabilities. AI algorithms can analyse electronic health records (EHRs) and genomic data to select the most suitable patients for trials, thereby increasing the success rate of the trials (MDPI). As data generation continues to expand outside traditional data centers, industries like healthcare stand to gain immensely from the power of edge computing. The ability to process data in real time will not only reduce the burden on centralized systems but also enable more informed, timely decision-making. 3. Inefficient Operations: Ballooning CostsAs AI workloads continue to grow, relying on traditional processors like GPUs and CPUs is leading to operational inefficiencies. Organizations are facing rising energy consumption and maintenance costs as a result. These inefficiencies put a strain on resources and make it harder to scale and maintain sustainability. ● Energy Consumption: Traditional GPUs and CPUs use a lot more power than specialized AI accelerators. For example, AMD's MI250 accelerators pull 500 W of power, with peaks reaching up to 560 W, while the MI300x uses a hefty 750 W (Forbes). ● Over-Provisioning: To meet performance demands, organizations often allocate more resources than needed—this practice is called over-provisioning. It leads to higher costs, especially in cloud environments where it can result in significant overspending (The Register). By switching to AI-specific chips, organizations can cut down on energy consumption, lower operational costs, and set themselves up for long-term efficiency and scalability. 4. Blackouts Due to Power ConsumptionThe rapid growth of artificial intelligence is driving up energy consumption, especially when it comes to training large AI models. Traditional processors, like GPUs, are major contributors to this energy demand, which has significant environmental consequences. ● Carbon Emissions: A study from the University of Massachusetts Amherst found that training a single large AI model can emit over 626,000 pounds of CO₂—roughly equivalent to the lifetime emissions of five cars (MIT Technology Review). ● Data Center Vulnerabilities: Data centers using outdated hardware are increasingly vulnerable to power outages and system failures. This is due to surges in energy consumption during heavy AI processing tasks. A report even predicts that U.S. data center power usage could nearly triple by 2028, highlighting the strain on existing infrastructure (Reuters). By implementing specialized AI chips, like Non-Von’s, organizations can improve power efficiency, reduce their environmental impact, and boost the reliability of their data center operations. The Future of AI Hinges on Specialized Chips The risks of sticking with traditional processors instead of switching to AI chips are simply too big to overlook. Failed edge computing, stranded data, inefficient operations, and blackouts aren’t just technical problems—they’re business-critical issues that can throw entire industries off track. Organizations that make the switch to AI chips now will gain a competitive edge, driving operational efficiency, cutting costs, and securing a more sustainable future. Sources:
by the Non-Von team
AI chip technology has become a competitive battleground as the demand grows for faster, more efficient, and scalable solutions. While companies like NVIDIA, Google, and Apple have led the charge, their legacy-laden technologies often fall short in critical areas. Non-Von is reshaping the field with a different kind of chip that meets today’s challenges head-on, setting a new benchmark in performance and sustainability. The Industry Challenge: AI is pushing the limits of current hardware. Chips must handle complex algorithms, reduce power consumption, and operate efficiently in real-time environments like autonomous systems and defense applications. Current leaders are facing critical shortcomings. For example:
Non-Von is addressing these challenges with a chip that offers unmatched efficiency for real-time edge AI, handling demanding tasks while drastically reducing power consumption. Non-Von’s chip can make data center inferencing orders of magnitude more efficient while also enabling edge applications where energy efficiency and performance are paramount. Head-to-Head: How Non-Von Outshines Its Competitors Energy Efficiency: Doing More with Less When selecting the right AI chip, energy efficiency is a key consideration. Edge AI tasks often require a delicate balance between performance and power consumption, especially when working with autonomous systems or real-time decision-making. Here's how Non-Von stacks up against NVIDIA, Google, and Apple: 1. NVIDIA Jetson Orin NanoPower Consumption: The Jetson Orin Nano is a compact AI chip that delivers up to 40 TOPS (Tera Operations Per Second) of performance while consuming 7 to 15 W (CRN). The Jetson Orin Nano is designed for edge AI applications, offering a balance of performance and efficiency for real-time tasks like autonomous vehicles or robotics. While it’s efficient in comparison to more powerful NVIDIA chips, it still requires much more power than Non-Von for similar workloads. 2. Google Coral Dev Board (Edge TPU)Power Consumption: The Google Coral Dev Board, powered by the Edge TPU, operates in the range of 2-4 W, optimized for AI inference at the edge (Extrapolate). While efficient for tasks like image recognition and object detection in IoT devices, the Coral Dev Board consumes significantly more power than Non-Von’s chip, which requires just .25W for comparable workloads. This makes Non-Von’s chip 400-800% more energy-efficient, a critical advantage for demanding applications in real-time edge AI and power-sensitive environments. 3. Apple A15 Bionic ChipPower Consumption: The Apple A15 Bionic chip is designed for mobile devices, offering high energy efficiency in tasks like photo processing and real-time video analysis. Exact power consumption for AI tasks isn’t publicly disclosed. (RedSwitches, extrapolate.com). Apple's chips are optimized for consumer-level AI applications, such as photo processing and mobile tasks, but they are not designed to handle the demands of industrial-grade AI or edge AI workloads. Although they offer energy efficiency, they lack the power and computational capacity needed for high-performance, real-time edge AI tasks that require continuous and intensive processing. 4. Non-VonPower Consumption: Non-Von’s chip is designed for maximum energy efficiency, consuming less than 1W at peak loads. Non-Von’s chip and model compiler excel in edge AI applications requiring both high performance and low power consumption. It is 400-800% (or greater) more efficient than leading competitors, making it ideal for energy-sensitive, real-time tasks like autonomous drones, wearable devices, and sensor-based systems. Sustainability: Reducing Environmental Impact NVIDIA: NVIDIA GPUs, especially those used in data centers, are known for their high power consumption, which can contribute to significant operational costs and increased natural resource demands, including water for cooling. Data centers, which house NVIDIA GPUs, already account for about 1-2% of global electricity consumption, and this consumption is expected to grow. Cooling systems, essential for maintaining optimal GPU performance, can consume up to 40% of a data center's total energy (Home of Energy News, NVIDIA Developer). Google: Google's reliance on cloud infrastructure and TPUs also brings significant environmental costs. While the company has made strides toward sustainability, the energy consumption and water usage required to power and cool large-scale data centers remain high. Google is working towards more sustainable practices, but its infrastructure still demands substantial energy for processing and cooling operations, impacting both cost and the environment (Google Cloud Blog). Apple: Apple focuses on reducing the environmental impact of its consumer devices, but its chips are not optimized for large-scale industrial AI applications or edge computing, where energy optimization is crucial. While Apple has implemented sustainability programs, its chips are not designed to meet the energy needs of high-performance AI tasks, like those used in autonomous systems or other real-time, edge AI applications (Tech Times, Sustainable Electronics Initiative) Non-Von: Non-Von’s chips offer a more sustainable solution by being energy-efficient, consuming significantly less power than the industry leaders. By drastically reducing power usage and the need for cooling, Non-Von’s chips help minimize the carbon footprint for industries like defense, robotics, and autonomous systems. These advantages make Non-Von a more environmentally-friendly alternative in the field of AI hardware. Flexibility: Ready for Today and Tomorrow NVIDIA: NVIDIA’s GPUs are general-purpose chips designed for a wide range of applications, including AI and machine learning tasks. However, they often require custom integration for specialized use cases like defense or autonomous systems, where specific performance needs must be met (such as low latency or real-time processing). While highly versatile, NVIDIA’s chips are primarily designed for data-heavy cloud applications rather than optimized for low-power, real-time edge use. As a result, while they perform well across many domains, they do require significant custom integration for niche applications (NVIDIA Newsroom, NVIDIA) Google: Google’s TPUs (Tensor Processing Units) are highly optimized for Google’s cloud infrastructure, excelling in tasks like machine learning and AI model training. However, TPUs are not as flexible as general-purpose GPUs and are not designed for use outside of Google’s infrastructure. This limits their effectiveness for a wide variety of real-world applications, especially when it comes to edge AI or situations that require low-latency, on-device processing. Their lack of flexibility makes them less adaptable compared to other chips that support a broader range of use cases (AI Stacked) Apple:Apple’s chips, such as the A15 Bionic or the M1, are highly efficient in consumer applications but are specifically designed for Apple’s ecosystem. They are powerful for tasks like photo processing, mobile AI, and video streaming, but they are not optimized for industrial-scale AI tasks that require high-performance edge processing or specialized applications like autonomous vehicles. Their design is tailored for consumer devices and lacks the scalability and flexibility needed for demanding industrial applications (Profolus, AnandTech) Non-Von:Non-Von’s chips are specifically built for parallel processing and AI model optimization, making them ideal for real-time, inference-specific applications like autonomous vehicles, wearable military tech, and sensor-based systems. They are optimized for sparse and unstructured models but flexible enough to provide efficiency gains for a wide variety of other types of currently popular models. This flexibility allows them to handle a wide variety of AI workloads, offering long-term scalability for future AI innovations. Unlike the general-purpose NVIDIA GPUs or cloud-based Google TPUs, Non-Von is focused on delivering high performance while maintaining energy efficiency and versatility, which makes it an ideal solution for demanding, energy-sensitive environments, both on the edge and in the data-center. Real-World Use Cases Non-Von’s edge in energy efficiency and adaptability isn’t theoretical—its been tested in multipel scenarios and has applications across a range of use cases.
While NVIDIA, Google, and Apple have built their success on specific strengths, Non-Von’s design offers:
The Future of AI Starts Here Non-Von’s chip doesn’t just introduce competition—it brings a revolution. By addressing the shortcomings of legacy-ridden technologies from companies like NVIDIA, Google, and Apple, Non-Von is proving that AI hardware can be powerful, efficient, and sustainable. Whether for defense, data centers, or edge AI, Non-Von is ready to lead the next generation of AI innovation. Take the Next Step Ready to learn how Non-Von can transform your AI capabilities? Submit your email at the bottom of www.non-von.com to receive our periodic updates and follow us on Linkedin today to explore partnership opportunities and see the difference for yourself. Sources:
Derek Odom, CEO Non-Von
As artificial intelligence (AI) technologies continue to advance and proliferate, the energy demands associated with current computing hardware (CPUs and GPUs) is also rapidly increasing. This growing energy consumption driven by AI leads to significant environmental challenges. New AI focused chip designs, like Non-Von’s, are a large part of the solution and are the focus of this post. Growing Energy Demands The rise of AI applications, especially generative AI, has led to a dramatic increase in energy consumption, particularly within data centers that support these technologies. According to the International Energy Agency, electricity demand from data centers is expected to more than double by 2026, largely due to AI applications (TIME). This surge in energy needs poses significant challenges, especially when much of this energy is derived from fossil fuels, undermining global climate change efforts (McKinsey & Company). The overwhelming energy requirements of AI can strain local infrastructures, resulting in brownouts and additional resource shortages, such as water (used to cool the data centers), exacerbating the urgency of addressing AI's energy consumption sustainably (TIME). The increasing energy demands point toward a steadily increasing carbon footprint for AI computing industry and the processors currently powering its growth. These environmental impacts, and their associated economic and national security implications, highlight the need for mitigating innovative solutions.(Yale E360). Environmental Challenges The environmental consequences of AI's energy demands are substantial. Critics emphasize that the growing carbon footprint associated with current AI processors could further exacerbate climate change (Goldman Sachs). While AI holds the potential to optimize energy usage and reduce emissions, the current trajectory of energy consumption may outpace available resources in many regions (Yale E360). The challenge lies in ensuring that the expansion of AI technologies does not compromise environmental sustainability. The Need for Sustainable Solutions To tackle these pressing challenges, collaboration between the energy and data center sectors is vital. Developing sustainable solutions, such as transitioning to renewable energy sources and enhancing energy efficiency, is critical (McKinsey & Company). Interestingly, AI can also contribute to its own solution. By leveraging AI to optimize energy use across various industries, companies and researchers can enhance overall energy efficiency, emphasizing the need for energy-efficient AI chips (Yale E360). Advantages of Non-Von’s Hardware/Software Solution Non-Von’s chips and supporting software ecosystem are engineered to overcome the limitations of traditional computing hardware. These chips boast enhanced computing power and improved energy efficiency, directly addressing the energy consumption challenges posed by standard chips. Non-Von uses advanced parallel processing capabilities, allowing them to perform complex AI functions while minimizing energy use. While easily implementing today’s popular models through its advanced compiler, Non-Von’s hardware also opens up computing to a whole new class of sparse, unstructured models. These types of models can enable additional efficiency gains currently unrealized due to constraints in today’s hardware. By prioritizing power efficiency, Non-Von chips are crucial in meeting the increasing energy demands associated with AI technologies. Future Implications As the demand for AI technologies continues to grow, the environmental impact of standard chips will become increasingly unsustainable. Transitioning to energy-efficient hardware/software solutions like Non-Von’s is essential for mitigating these impacts and achieving sustainability goals. Continued innovation and investment in AI chip technology is a necessity in aligning technological advancements with environmental sustainability. By emphasizing energy-efficient designs and harnessing AI to optimize energy use, the industry can ensure that the benefits of AI do not come at the expense of our planet's health. Hope for a sustainable future with AI The environmental impact of standard chips presents significant challenges as the world moves toward an AI-driven future. However, with innovative solutions like Non-Von’s, there is hope for a more sustainable technological landscape. By addressing energy consumption and prioritizing efficiency, we can harness the power of AI while protecting our environment for future generations (not to mention the economic benefits of reduced electrical requirements). Sign up for our newsletter at the bottom of our homepage (non-von.com) and become part of shaping the future of AI with us. Sources:
Richard Granger, PhD, President Non-Von Eli Bowen, PhD, CTO Non-Von Non-Von chips reduce power costs by two orders of magnitude (100x) for the growing set of sparse, quantized AI systems. Non-Von's power efficient chips both i) enable low-power untethered edge systems such as sensors, and ii) provide enormous power savings for server-level AI. Current AI systems typically run on GPUs, first designed decades ago for screen graphics processing. To work fast, GPUs rely on high power consumption, burdening current AI systems with immense electric budgets (1). Currently, many companies are scrambling to hire AI and data science coders, to construct or adapt large models, and then *lose money* on the resulting power bills. Small parts of this burden can sometimes be alleviated with software changes alone. Many companies are making systems sparse (reducing redundant data), and quantized or compacted (using lowest-precision encodings), such as BitNet and Hamiltonian Net (2). Orders of magnitude more savings arise by using non-GPU hardware that is directly fitted to these sparse and compacted software methods. Some chips are designed for particular software examples, such as convolutional nets or transformers, but Non-Von's NV1 chip specifically targets the large and growing class of sparse compacted systems. From first principles, Non-Von has designed a hardware-software solution called SCULPT - sparse compacted ultra low-power toolset for AI, "sculpting away" whatever the model does not need, refactoring it to take maximal advantage of Non-Von hardware. Any existing model software can be SCULPTed with just a few lines of code, yielding large gains when implemented on Non-Von hardware. By contrast, standard GPUs and von-Neumann architectures prevent efficient code execution, greatly adding to the costs of implementing and running large AI systems. The Non-Von hardware-software SCULPT architecture allows AI designs that immediately run efficiently, with enormous cost savings. Non-Von can take any trained model, including all common model development packages (Pytorch, ONNX, Tensorflow, Keras, SKLearn, and many more), and automatically convert it to a sparse compacted system implemented on silicon. These can be printed as custom Non-Von silicon, or imprinted into Non-Von's mass-produced programmable AI accelerator boards. The results are 100 times more power-efficient on tasks ranging from deep-learning-based visual recognition to transformer-based chatbots (3). Sources:
Derek Odom, CEO Non-Von
The limits of current hardware prove particularly stark in the military and national security context. Not only are today’s tactical edge devices limited by the generally low computational capacity of current hardware, but any edge device on the battlefield also must limit its use of cloud communication, to evade enemy targeting of the device. Furthermore, the high power budgets of current edge AI hardware limits the duration of all types of autonomous devices from stand-alone sensors to air/land/sea/space unmanned vehicles. These compute and power constraints often mean the ability to use advanced AI generally requires a cloud connection. This requires commanders to assess the risk of leveraging AI capabilities with the risk of compromise. Together, these needs constitute an enormous demand for new approaches to AI at the edge. The recognition that today’s hardware technology must advance is not a novel idea. Billions of dollars are pouring into developing advanced processors for AI applications from the edge to the data center. But most of these "new" solutions are still just incremental fixes to existing technology. The underlying architectural principal remains von Neumann’s 80-year-old design. Right now, the industry reminds us of software companies that refuse to start over and instead build ever more convoluted solutions to advance their products. (Windows anyone?). We propose a different solution, a processor-and-software ecosystem intrinsically designed for AI. We have constructed a new paradigm to break away from the constraining GPU world: a new chip architecture that enables efficiencies that the GPU ecosystem cannot take advantage of. We start with the NV1 architecture, that already outperforms current edge GPUs on AI tasks [3], and encompasses a software ecosystem to enable algorithms to work far more effectively and efficiently than GPUs allow. An innovative communication protocol allows Non-Von to minimize power usage, and data transport costs among nodes are vastly reduced by eliminating the address bus, through local target address matching [3]. Non-Von's new chips open the door for expanded classes of models, particularly suited to efficient edge processing. Join our mailing list at the bottom of our home page (non-von.com), and help us build the future of AI. Sources:
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