Highly scalable, area and power efficient machine learning accelerator core
A critical requirement for the next wave of edge applications is advanced
processing and machine learning capabilities. Machine learning use cases vary
widely for the different markets and application domains requiring different
amounts of acceleration compute performance and at differing amounts of power
dissipation and overall solution cost.
One of the most effective ways to offer improved compute performance and power
efficiency for machine learning applications is to integrate a purpose-built
and dedicated neural processing unit (NPU), sometimes also referred to as a
machine learning accelerator (MLA) or deep learning accelerators (DLA), into
the device to complement the CPU compute cores.
NXP offers a very wide portfolio of devices from traditional MCUs in the
Kinetis, LPC families and more recently the MCX portfolio of devices, to our
i.MX RT crossover MCUs and our i.MX applications processors, and in each of
the market areas we serve, we see an increased demand for efficient machine
learning compute capabilities. To offer highly- optimized devices to our users
across our portfolio, we developed the eIQ Neutron neural processing unit
(NPU). The eIQ Neutron NPU architecture scales from the most efficient MCU to
the most capable i.MX applications processors in our portfolio. This billions
(Giga) to trillions (Tera) operations per cycle scalability combined with the
support for a wide variety of neural network types such as CNN, RNN, TCN and
Transformer networks and more is a recipe for success.
Figure 1: eIQ Neutron NPU block diagram
The eIQ Neutron NPU offers a rich set of options that can be leveraged based
on the NXP edge processing device the core is integrated into and the market
needs that device family is addressing.
- Dedicated controller core
- In-line dequantization, activation and pooling
Built in tiny-caching to reduce power consumption and reduce reliance on
system memory speed
- Weight decompression engine
Advanced multi-dimensional DMA for input and output formats, including
striding, batching, interleaving, concatenating
- Configurable coupled memory
Further to the hardware capabilities and features, the eIQ Neutron NPU cores
are fully supported by the award winning eIQ® machine learning
software development environment.
The combination of NXP developed hardware acceleration and software enablement
offers our users the ability to leverage their experience across the NXP edge
processing portfolio as well as the reassurance that support for emerging
machine learning neural networks, models and operators can be more efficiently
supported even after devices are deployed and in the field.
You can start to develop intelligent solutions with the eIQ Neutron NPU with
the MCX-N series of MCUs and the i.MX 95 applications processors with more
devices to come.
Explore eIQ Neutron NPU on MCX N MCUs:
i.MX 95 Family of Applications Processors: