Limits

v​The limits of current AI hardware
     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. 
 
 
[1] “AI Is Poised to Drive 160% Increase in Data Center Power Demand.”
    https://www.goldmansachs.com/insights/articles/AI-poised-to-drive-160-increase-in-power-demand
    https://www.nature.com/articles/d41586-024-00478-x
    https://www.theatlantic.com/technology/archive/2024/03/ai-water-climate-microsoft/677602/
    https://www.wsj.com/business/energy-oil/ai-is-about-to-boost-power-billswholl-take-heat-for-that-c527f27b?mod=panda_wsj_custom_topic_alert
 
[2] Singh and Gill, “Edge AI.”
    https://www.sciencedirect.com/science/article/pii/S2667345223000196
   
[3] Hokenmeier et al. (2024) IEEE 33rd Microelectronics Design & Test Symposium (MDTS)
    doi.org/10.1109/MDTS61600.2024.10570137