Ludwig Computing has developed a new computing system that operates more like the physical world than the clock-driven digital chips. Via algorithm-hardware co-design, Ludwig Computing uses decades of combined experience to leverage the natural physics of devices to solve complex AI and probabilistic problems efficiently.
FELLOWS
Behtash Behin-Aein
Behtash Behin-Aein, founder and CEO of Ludwig Computing, is renowned for his trailblazing spintronics research at Purdue University, highlighted in Nature Nanotechnology. His leadership at GlobalFoundries was instrumental in the STT-MRAM model development, contributing to the 40Mb chip’s success. As Semiconductor Research Corporation’s C-SPIN industry liaison leader, he effectively bridged academic research with industry, earning the 2017 Mahboob Khan Award. Behin-Aein pioneered probabilistic computing alongside Supriyo Datta at Purdue University, harnessing p-bits for enhanced chip efficiency and speed.
TECHNOLOGY
Critical Need
Data centers and information and communication technologies, including personal devices such as computers and mobile phones, are on track to consume more than 20 percent of global electricity by 2030. Plus, scaling today’s computers based on Moore’s law presents daunting challenges, limiting performance improvements with reasonable costs.
At the same time, many industries are looking to probabilistic computing, which uses statistical methods as a means of solving problems, to improve their businesses. Running probabilistic computing on general-purpose microprocessors, however, means that the benefits it offers to industry come at the expense of increased silicon footprint and energy consumption. Efficient domain-specific accelerators can rectify this problem.
Technology Vision
Probabilistic computing makes it possible to process uncertainties inherent in data or leverage randomness to interpret, infer, and make better decisions faster and more efficiently than conventional computing.
Probabilistic co-processors are domain-specific accelerators designed to execute probabilistic computing algorithms faster while consuming less space and energy than conventional general-purpose chips.
Rather than shrinking devices to pack more into a given chip area, Ludwig Computing is reducing the number of devices and simplifying the circuits by changing the information processing paradigm to increase computational power.
Ludwig Computing is developing its probabilistic co-processors for applications in Generative AI and complex optimization problems.
Potential for Impact
This technology could create a new paradigm for faster and more efficient implementations of computationally intensive probabilistic algorithms. It will enable the computations on wearable devices and at the edge instead of in the cloud. It will reduce latency and allow real-time applications either not possible or too cost/power prohibitive today.
Probabilistic computing can improve compute performance without increasing transistor count—an increasingly complex task—and can usher in a new era of progress.