Tech Briefs Magazine - March 2022 - Sensor-14

Applications
ers together the inputs of many sensors.
This is critical for automotive use because
each sensor has its own limitations.
For example:
* Cameras can have very high resolution
so they can sense fine details. But you
typically need more than one to cover
vehicle blind spots, rear view, surround
view, and several front facing cameras
with different focal lengths. Each camera
will be providing a separate data
stream. Also, cameras can't be relied
upon to function well at night or if
they are facing into the sun.
* Radar functions well at night and can
provide distance measurements. But it
does not have high enough resolution
to perceive the precise location of an
object or distinguish between multiple
objects that are close to each other. It
can fail to detect stationary or slowmoving
objects and also cannot detect
the " semantics " of the scene: color and
shape of objects - for that you need
cameras.
* Lidar acts as its own light source so it
performs well both in darkness and
daylight. It also provides rapid and
accurate measurement data with high
enough resolution for precise real-time
free-space detection while tracking
multiple objects within a scene. But in
order to have enough lidar data points
you'd need five or six of these expensive
devices mounted on a vehicle.
The SensPro DSP can take inputs
from these, as well as time of flight (ToF)
sensors, inertial measurement units
(IMUs), efficiently process algorithms
for simultaneous localization and mapping
(SLAM) and using them all, can
create " contextual awareness " - a full
picture of the situation of the automobile
and its environment. You can also
add a dedicated instruction set in the
hardware to get additional acceleration
of other specific processing if needed.
Integrating the System
NeuPro-M addresses many of the key
challenges of autonomous vehicle functions
including high speed and low latency
scalable operation, low operating
power, high security, and the ability to
meet the functional requirements of the
ISO 26262 standard for safety-related
electrical/electronic systems in production
road vehicles. It is also scalable, so
for example, it can be used for single sensors,
clusters of sensors in a zone, or it
can even be embedded as part of the
automobile's engine control unit (ECU).
14
Cov
The Architecture of a HighPerforming
Automotive AI Platform
The function of the CEVA NeuPro-M
AI processor is to make high-level driving
decisions. These decisions are based
on the DNN inputs that provide situational
information, such as: Are there
oncoming cars; is the automobile staying
in lane; what is the absolute location
(SLAM)?
The NeuPro-M AI processor is the
decision-maker as to what actions to take,
given the information it receives. AI
requires a massive number of calculations
to make decisions, and these must
be done in close to real time. It therefore
takes a powerful processor. For automotive
applications, it is also necessary to
minimize power consumption - as the
number of calculations increases and the
time to perform them decreases, the
power consumed increases. The measure
of the processor's performance can be
summed up in one number: tera operations
per second per watt (TOPS/watt)
- the higher the number, the better the
performance. The power efficiency of
the NeuPro-M is 24 TOPS/watt, which is
significantly higher than most other
automotive AI processors.
Security is of utmost importance - in
fact, you can say that security and safety
are the most important requirements for
automotive applications. The AI processor
in an autonomous vehicle is the driver-in-charge.
The neural networks that
perform the AI processing assign weights
to each of their inputs, and those weights
are vulnerable to malicious tampering.
Security systems are therefore an integral
piece of the processor in order to guard
against those attacks.
NeuPro-M Block Diagram
NeuPro-M Core
Engine #8
NeuPro-M
by CEVA
NPM Common Subsystem
Functional Safety
Multi
Engine
Controller
Security Logic
Data & Weights
Compression/
Decompression
Core
Shared
Memory
ISP
Interface
AXI
Host
Interface
AXI Matrix
AXI
Mixed
Precision
Neural Engine
4K
MAC
Complementary
Activation
Engine
NeuPro-M Master Controller
-Optimized for performance and code size
NPM Engine
Engine Local
Controller
-Engine flow
-Matrix decompostion
Unstructured
Sparsity
Engine
Unstructured
Sparsity
Engine
Vector
Processor
Unit
Winograd
Transform
Engine
Engine #...
Engine #2
Engine #1
Optimizing Performance
For optimizing performance, it's
important to realize that no matter how
fast the processor is, the system can run
into a bottleneck because of bandwidth
limitations on the movement of information
to the processor, primarily because
of system interfacing limitations in terms
of memory. The computation units work
much faster than the time it takes to
store and fetch the huge amounts of
data to inform the memory. One way
this can be addressed is by directing a
continuous flow of data to the processor
rather than waiting for enough information
to perform a particular calculation.
Another way performance can be optimized
is to be adaptive - to use a modular
adaptive topology. The processor topologies
can be optimized for processing different
types of sensors and performing different
types of operations. One function,
for example, might be to optimize the
powertrain efficiency, which requires a lot
of mathematics, or on the other hand it
might just be processing a single sensor.
If you need to calculate something
very accurately, you could use floatingpoint
arithmetic instead of fixed-point,
inside a vector processor. Another way to
address bandwidth reduction, is to compress
the data, so you don't have to move
all of it in the system. These are software
fixes, but you also have to look to optimizing
the DSPs and the AI processor, as
well, by doing a deep dive to address all
of the bottlenecks in each of these areas.
Parallel Processing
The NeuPro-M (NPM) processor has
three parts (See Figure 2): The master
controller; the NPM common subsysFigure
2 (Image: CEVA)
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Sensor Technology, March 2022
Engine local
Shared
Memory
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Tech Briefs Magazine - March 2022

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