Introduction
The rapid proliferation of artificial intelligence (AI)
is reshaping industries and transforming how we
interact
with technology. As AI capabilities expand,
processing data at the edge—closer to where data
are generated
and used—is becoming more prevalent.
In fact, recent analyst reports suggest that
about 75% of the world's data will be generated
at the edge, marking a substantial change from
traditional centralized data processing models.
By deploying AI on edge devices, systems can
perform real-time data processing and decision-
making without the latency and security concerns
associated with cloud connectivity.
Now, as AI workloads become more complex and
diverse, memory considerations are no longer an
afterthought in system design. Instead, memory
is an integral component that can significantly
impact the performance, power efficiency, and
overall capabilities of edge AI systems.
AI applications, particularly at the edge, are
extremely memory-hungry; as such, they require
high-performance, low-latency memory solutions
to handle the massive amounts of data involved in
AI inference. Such demands are pushing memory
technology to new frontiers, with solutions like high-
bandwidth memory and low-power double data rate
(LPDDR) memory becoming increasingly important.
This shift has elevated memory's importance
in the AI ecosystem from a commodity to a key
differentiator in AI-enabled devices and applications.
This eBook will examine the importance of memory
in edge AI applications, the design considerations
associated with deploying edge AI, and the ways
that Micron Technology is leading the charge with
the industry's highest-performance and highest-
density memory solutions.
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5 Experts on Addressing the Hidden Challenges of Embedding Edge AI into End Products
