HW-Accelerated Physical AI Framework For Resource-Constrained Edge Devices (ASU)


A new technical paper titled "Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics" was published by researchers at Arizona State University. Abstract "Physical AI at the edge—enabling autonomous systems to understand and predict real-world dynamics in realtime—demands efficient hardware acceleration. Model recovery (MR), which extracts governing equations ... » read more

Software Controlled Modular FPGA


Flex Logix has developed embedded FPGA IP (EFLX® embedded FPGA or eFPGA) that has been licensed for use in many commercial, aerospace and defense programs. It has also developed an edge inferencing accelerator, InferX® to efficiently process AI edge inferencing workloads requiring high throughput for the least power and area. This paper describes managing and dynamically programming eFPGA des... » read more

Basics Of Embedded FPGA Acceleration


Making a chip run faster is no longer guaranteed by shrinking features or moving to a different manufacturing process. It now requires a fundamental change in the architecture of the chip itself. The days of the single-processor, or even single multi-core processors, are gone. The focus has shifted to different kinds of processors for different kinds of data and many different protocols and ... » read more