Optimized compilers are critical to enable high-performance machine learning at the lowest power for the embedded edge. I’m thrilled to announce that Dr. Randy Allen, an acknowledged compiler technologist and a seasoned SW leader, has joined us to lead our path-breaking software innovations and accelerate customer readiness of our Machine Learning SoC (MLSoC) technology.

Randy is a unique technologist with over 30 years of experience developing advanced optimizing compilers for parallel, heterogeneous targets and managing large multi-site software teams. His more notable technical accomplishments include developing in his doctoral dissertation the theory underlying vectorizing and parallelizing compilers as well as participating directly in the development of many early commercial compilers, writing major portions of VCS (the world’s first and most widely-used compiled Verilog simulator), and developing a highly-optimizing fixed-point Matlab compiler targeted to DSPs.

A veteran of many startups, Randy has founded two companies and co-authored an influential technology book, “Optimizing Compilers for Modern Architectures”, the de facto standard text for graduate level advanced compiler optimization courses.

Randy joins us from SiFive, where he was Vice President of Software Engineering and has worked in a variety of leadership roles at Mentor Graphics, National Instruments, Cypress Semiconductors and Synopsys. Randy earned his bachelor’s degree in Chemistry from Harvard and his Ph.D. in Mathematical Sciences from Rice. He also holds two technology patents in fixed-point arithmetic and is known in the industry for his ability to balance theory and pragmatism to resolve hard problems, regardless of whether the problems are technical, personnel, or strategic in nature.

At SiMa.ai, Randy will manage end-to-end delivery of compilers and tool chains for our ML accelerator. Computational performance and power efficiency are sine qua non to successful ML execution, and Randy will develop software tools that deliver world-class performance and power efficiency. These tools will enable SiMa.ai hardware to deliver unparalleled performance and power efficiency for ML applications such as untethered robotics, AI-enabled surveillance, and autonomous automobiles.

“Having worked with some of the most advanced technology companies in the world, I have learned first-hand how to tackle hard technical problems, distill away extraneous stuff to derive the underpinnings of a simple, unifying solution, and pragmatically implement that solution. I get stuff done and make sure it’s done right,” said Dr. Allen. ”I’m excited to bring my skill set to SiMai.ai to accelerate our innovation in high-performance machine learning solutions at the embedded edge.”

With Randy’s leadership combined with our team of advanced engineers, we will be able to execute on our vision more quickly — to disrupt the $40B embedded edge market and enable large scale deployment of machine learning (ML) to our growing customer base.