NXP Semiconductors N.V. (NASDAQ: NXPI) today 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
GlowIn 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
AdvantageThe 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.”
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.
Accelerated Performance with NXP’s Glow Neural Network
ImplementationeIQ 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.
“The Tensilica HiFi 4 DSP was originally integrated in the i.MX
RT600 crossover MCU to accelerate a broad range of audio and voice
processing applications. However, as the number of ML inference
applications targeting low-cost, low-power MCU-class applications
has increased, the inherent DSP computational performance of the
HiFi 4 DSP makes it an ideal target to accelerate these NN models,”
said Sanjive Agarwala, corporate VP, Tensilica IP at Cadence.
“Through NXP’s Glow implementation in eIQ ML software, customers of
i.MX RT600 MCUs can leverage the DSP to address a number of ML
applications including keyword spotting (KWS), voice recognition,
noise reduction and anomaly detection.”
“NXP’s inclusion of the Arm CMSIS-NN software library in elQ is
designed to maximize the performance and minimize the memory
footprint of neural networks on Arm Cortex-M cores,” said Dennis
Laudick, VP Marketing, Machine Learning at Arm. “Using a CIFAR-10
neural network model as an example, NXP is able to achieve a 1.8x
performance advantage with CMSIS-NN. Other NN models should yield
similar results, clearly demonstrating the benefits of this
advanced compiler and our optimized NN operator library.”
AvailabilityNXP’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.
About the i.MX RT Series of Crossover
MCUsThe i.MX RT series is the industry's first
crossover MCU portfolio, featuring a high-performance Arm Cortex-M
core, real-time functionality and MCU usability at
an affordable price. The series represents the convergence of
low-power applications processors and high-performance
microcontrollers. The i.MX RT series bridges the gap between the
traditional MCUs and i.MX applications processor space, allowing
MCU customers a path for significant performance and integration
improvements, without sacrificing ease-of-use.
For more information, go to www.nxp.com/eiq and
www.nxp.com/eiq/glow
About NXP Semiconductors NXP
Semiconductors N.V. enables secure connections for a smarter world,
advancing solutions that make lives easier, better, and safer. As
the world leader in secure connectivity solutions for embedded
applications, NXP is driving innovation in the automotive,
industrial & IoT, mobile, and communication infrastructure
markets. Built on more than 60 years of combined experience and
expertise, the company has approximately 29,000 employees in more
than 30 countries and posted revenue of $8.88 billion in 2019. Find
out more at www.nxp.com. NXP, EdgeVerse, and the NXP logo are
trademarks of NXP B.V. All other products or service names are the
property of their respective owners. All rights reserved. © 2020
NXP B.V.
For more information, please
contact:
America and
Europe |
Greater China /
Asia |
Jason Deal |
Ming Yue |
Tel: +44 7715228414 |
Tel: +86 21 2205 2690 |
Email: jason.deal@nxp.com |
Email: ming.yue@nxp.com |
NXP-IoT
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