To further confound matters, while high-performance
memory is desirable for AI applications, cost remains
a significant factor, especially in mass-market
embedded systems. Designers must carefully weigh
the benefits of faster, more expensive memory
against budget constraints and the application's
specific requirements. In some cases, a hybrid
approach may be optimal, using a combination of
high-speed memory for mission-critical AI tasks and
lower-cost memory for less demanding operations.
Such an approach can help optimize the cost-
performance ratio of the overall system.
Beyond raw performance metrics, edge AI designs
must also consider environmental factors. In real-
world deployed systems, considerations such as
operating environment, longevity, and reliability are
central to overall system reliability and performance.
For example, these systems may need to function
in challenging conditions, including under extreme
temperatures, high humidity, or shock and vibration.
As a result, memory solutions for edge AI must
often be ruggedized and designed for extended
operational lifespans.
Bringing AI to the edge is so
difficult because a bunch of
system challenges are behind
it. You have to consider the type
of AI, where the AI happens, the
type of processing and computing
required, and the type of memory
that goes with it."
David Henderson
Sr. Director, Industrial & Multi-Market Segment,
Micron Technology
C h a p t e r 2 | D e s i g n C o n s i d e r a t i o n s f o r E m b e d d e d A I
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5 Experts on Addressing the Hidden Challenges of Embedding Edge AI into End Products
