NXP Semiconductors has released its eIQ Machine Learning (ML) software support for Glow neural network (NN) compiler, delivering the industry’s first NN compiler implementation for higher performance with low memory footprint on NXP’s i.MX RT crossover MCUs.
As developed by Facebook, Glow can integrate target-specific optimizations, and NXP leveraged this ability using NN operator libraries for Arm Cortex-M cores and the Cadence Tensilica HiFi 4 DSP, maximizing the inferencing performance of its i.MX RT685 and i.MX RT1050 and RT1060. Furthermore, this capability is merged into NXP’s eIQ Machine Learning Software Development Environment, freely available within NXP’s MCUXpresso SDK.
Exploiting MCU Architectural Features using Glow
In May 2018, Facebook, the leading pioneer of PyTorch, introduced Glow (the Graph Lowering NN compiler) as an open source community project, with the goal of providing optimizations to accelerate neural network performance on a range of hardware platforms. As an NN compiler, Glow takes in an unoptimized neural network and generates highly optimized code. This differs from the typical neural network model processing whereby a just-in-time compilation is leveraged, which demands more performance and adds memory overhead. Directly running optimized code, like that possible with Glow, greatly reduces the processing and memory requirements. NXP has also taken an active role within the Glow open source community to help drive broad acceptance of new Glow features.
“The standard, out-of-the-box version of Glow from GitHub is device agnostic to give users the flexibility to compile neural network models for basic architectures of interest, including the Arm Cortex-A and Cortex-M cores, as well as RISC-V architectures,” said Dwarak Rajagopal, Software Engineering Manager at Facebook. “By using purpose-built software libraries that exploit the compute elements of their MCUs and delivering a 2-3x performance increase, NXP has demonstrated the wide-ranging benefits of using the Glow NN compiler for machine learning applications, from high-end cloud-based machines to low-cost embedded platforms.”
Optimized Machine Learning Frameworks for Competitive Advantage
The demand for ML applications is expected to increase significantly in the years ahead. TIRIAS Research forecasts that 98% of all edge devices will use some form of machine learning/artificial intelligence by 2025. Based on market projections, 18-25 billion devices are expected to include ML capabilities, even without dedicated ML accelerators, in that time frame. Consumer device manufacturers and embedded IoT developers will need optimized ML frameworks for low-power edge embedded applications using MCUs.
“NXP is driving the enablement of machine learning capabilities on edge devices, leveraging the robust capabilities of our highly integrated i.MX application processors and high performance i.MX RT crossover MCUs with our eIQ ML software framework,” said Ron Martino, Senior Vice President and General Manager, NXP Semiconductors. “The addition of Glow support for our i.MX RT series of crossover MCUs allows our customers to compile deep neural network models and give their applications a competitive advantage.”