NXP Semiconductors N.V. 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.
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’s edge intelligence environment solution for ML is a comprehensive toolkit that provides the building blocks that developers need to efficiently implement ML in edge devices. With the merging of Glow into eIQ software, ML developers will now have a comprehensive, high-performance framework that is scalable across NXP’s edge processing solutions that include the i.MX RT crossover MCUs and i.MX 8 application processors. Customers will be better equipped to develop ML voice applications, object recognition and facial recognition, among other applications, on i.MX RT MCUs and i.MX application processors.
eIQ now includes inferencing support for both Glow and TensorFlow Lite, for which NXP routinely performs benchmarking activities to measure performance. MCU benchmarks include standard NN models, such as CIFAR-10. Using a CIFAR-10 model as an example, the benchmark data acquired by NXP shows how to leverage the performance advantage of the i.MX RT1060 device (with 600MHz Arm Cortex-M7), i.MX RT1170 device (with 1GHz Arm Cortex-M7), and i.MX RT685 device (with 600 MHz Cadence Tensilica HiFi 4 DSP).
NXP’s enablement for Glow is tightly coupled with the Neural Network Library (NNLib) that Cadence provides for its Tensilica HiFi 4 DSP delivering 4.8GMACs of performance. In the same CIFAR-10 example, NXP implementation of Glow achieves a 25x performance advantage by using this DSP to accelerate the NN operations.
NXP’s eIQ for Glow NN compiler is available now, delivered via MCUXpresso SDK for i.MX RT600 Crossover MCUs, as well as i.MX RT1050 and i.MX RT1060 crossover MCUs. eIQ for Glow NN compiler will be available for other NXP MCUs in the future.
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