Overall, the right balance between all
these variables depends largely on the
type of AI inference being performed
at the edge. For example, complex
image recognition or natural language
processing inference may require more
sophisticated neural networks and larger
model sizes, necessitating higher memory
bandwidth and capacity. In contrast,
simpler sensor-based inference tasks
may have lower memory requirements but
demand faster response times.
Ultimately, when selecting memory
solutions and designing overall system
architectures for edge AI applications,
designers must carefully consider
the specific inference workload,
its performance requirements, and
operational constraints.
Micron is helping designers navigate
the design considerations of edge AI
systems by
• Offering a comprehensive range of high-
performance, low-power memory solutions
optimized for edge AI applications
• Collaborating with leading chipset
manufacturers to ensure memory
compatibility and performance
optimization for next-generation
AI platforms
• Providing expertise on memory
technology selection and balancing
factors like bandwidth, capacity, and
power efficiency for specific AI workloads
• Developing ruggedized memory options
that meet the stringent reliability and
longevity requirements of industrial
edge AI deployments
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
Many factors need to be
considered when designing
edge AI solutions, including
careful overview of integration
complexity, scalability, and market
considerations, to name a few."
Mark Harvey
Principal FAE, SiMa.ai
13
5 Experts on Addressing the Hidden Challenges of Embedding Edge AI into End Products
