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Energy-efficient AI hardware, particularly through neuromorphic circuits,

improves low-latency processing and enhances privacy, tackling design

challenges for IoT applications. Key highlights include:

Low Latency: Local AI processing reduces response times, crucial for real-
time applications.

Energy Efficiency: Local computations cut energy consumption by avoiding

cloud transfers, benefiting battery-operated devices.

Enhanced Privacy: On-device data processing minimizes breaches associated

with cloud storage, elevating user trust.

Neuromorphic Circuits: Non-Von Neumann architectures facilitate parallel

processing, addressing traditional design bottlenecks for better AI
performance.

-Scalable Design: Multi-core configurations offer flexibility for diverse

applications.

-Integrated Approach: Successful neuromorphic computing requires co-

design of hardware, software, and algorithms.

-Benchmarking: Establishing application-specific metrics aids in evaluating AI

hardware performance effectively.

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Energy-efficient AI hardware, particularly through neuromorphic circuits,

improves low-latency processing and enhances privacy, tackling design

Energy Efficiency: Local computations cut energy consumption by avoiding

Enhanced Privacy: On-device data processing minimizes breaches associated

Neuromorphic Circuits: Non-Von Neumann architectures facilitate parallel

-Scalable Design: Multi-core configurations offer flexibility for diverse

-Integrated Approach: Successful neuromorphic computing requires co-

-Benchmarking: Establishing application-specific metrics aids in evaluating AI

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