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differently for each layer of the

neural network to deliver the required

performance with the maximum

possible efficiency.

Memory Architecture

As well as improving compute efficiency

by varying inferencing precision,

configuring both the bandwidth

and structure of programmable on-

chip memories can further enhance

the performance and efficiency of

embedded AIs. A customized MPSoC

can have more than four times the on-

chipmemory, and six times thememory-

interface bandwidth of a conventional

compute platform running the same

inference engine. The configurability

of the memory allows users to reduce

bottlenecks and optimize utilization

of the chip’s resources. In addition,

a typical subsystem has only limited

cache integrated on-chip and must

interact frequently with off-chip

storage, which adds to latency and

power consumption. In an MPSoC, most

memory exchanges can occur on-chip,

which is not only faster but also saves

over 99% of the power consumed by

off-chip memory interactions.

Silicon Area

Solution size is also becoming an

increasingly important consideration,

especially for mobile AI on-board

drones, robots, or autonomous/self-

driving vehicles. The inference engine

implemented in the FPGA fabric of an

MPSoC can occupy as little as one-eighth

of the silicon area of a conventional

SoC, allowing developers to build more

powerful engines within smaller devices.

Moreover, MPSoC device families can

offer designers a variety of choices

to implement the inference engine

in the most power-, cost-, and size-

efficient option capable of meeting

system performance requirements.

There are also automotive-qualified

parts with hardware functional-safety

features certified according to industry-

standard ISO 26262 ASIL-C safety

specifications, which is very important

for autonomous-driving applications. An

example is Xilinx’s Automotive XA Zynq

UltraScale+™ family, which contains a

64-bit quad-core ARM

Cortex™-A53

and dual-core ARM Cortex-R5 based

processing system alongside the

scalable programmable logic fabric,

giving the opportunity to consolidate

control processing, machine-learning

algorithms, and safety circuits with fault

tolerance in a single chip.

Today, an embedded inference engine

can be implemented in a single MPSoC

device, and consume as little as 2 Watts,

which is a suitable power budget for

applications such as mobile robotics

or autonomous driving. Conventional

compute platforms cannot run real-time

CNN applications at these power levels

even now, and are unlikely to be able

to satisfy the increasingly stringent

demands for faster response and more

sophisticated functionality within more

challenging power constraints in the

future. Platforms based on programmable

MPSoCs can provide greater compute

performance, increased efficiency, and

size/weight advantages at power levels

above 15W, too.

The advantages of such a configurable,

multi-parallel compute architecture

would be of academic interest only, were

developers unable to apply them easily

in their own projects. Success depends

on suitable tools to help developers

optimize the implementation of their

target inference engine. To meet this

need, Xilinx continues to extend its

ecosystem of development tools and

machine-learning software stacks, and

working with specialist partners to

simplify and accelerate implementation

of applications such as computer vision

and video surveillance.

Flexibility for the Future

Leveraging the SoC’s configurability

to create an optimal platform for

an application at hand also gives AI

developers flexibility to keep pace with

the rapid evolution of neural network

architectures. The potential for the

industry to migrate to new types of

neural networks represents a significant

risk for platform developers. The

reconfigurable MPSoC gives developers

flexibility to respond to changes in the

way neural networks are architected,

by reconfiguring to build the most

efficient processing engine using any

contemporary state-of-the-art strategy.

More and more, AI is being embedded

in equipment such as industrial controls,

medical devices, security systems,

robotics and autonomous vehicles.

Adaptive

acceleration

leveraging

programmable logic fabric MPSoC

devices holds the key to delivering the

responsive and advanced functionality

required to remain competitive.

New-Tech Magazine Europe l 27