Called MISIM (which stands for “machine inferred code similarity”), the new “machine programming system” developed by Intel in conjunction with MIT and Georgia Tech is an automated engine using neural networks to learn what a piece of software intends to do, by studying the structure of the code and analyzing syntactic differences of other code with similar behavior. As explained in a press release, Intel’s ultimate goal for machine programming is to enable software creation based on human intention expressed in any fashions, whether that’s code, natural language or something else. From an EDA perspective, it will be interesting to see if some aspects of this AI-based code analysis will prove applicable to HDL in chip design, too.

Credit: Intel

NXP microcontrollers gain Glow neural network compiler

NXP’s eIQ Machine Learning Software Development Environment now supports the Glow neural network compiler, with the goal of delivering high performance inferencing for NXP’s i.MX RT series of crossover MCUs – especially for vision and voice applications at the edge. NXP’s implementation of Glow targets Arm Cortex-M cores and the Cadence Tensilica HiFi 4 DSP, with platform-specific optimizations for the above-mentioned series of NXP products. Glow (the Graph Lowering NN compiler) was introduced by Facebook in May 2018 as an open source community project, with the goal of providing optimizations to accelerate neural network performance on a range of hardware platforms. The term “crossover” used by NXP to designate this MCU series refers to the convergence of low-power applications processors and high-performance microcontrollers. Besides Glow, the NXP’s eIQ Machine Learning Software Development Environment also includes inferencing support for TensorFlow Lite.
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