Optimizing Heterogeneous Computing at Edge AI
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Optimizing Heterogeneous Computing at Edge AI


AI is bringing revolutionary change across
industries and business including agriculture, transportation, manufacturing, and more. The potential of AI is astronomical; and deploying AI at the edge has many benefits. Edge AI delivers faster responses
by eliminating the need to send data to the cloud for AI processing. Security is enhanced by decreasing
the risk of data tampering during transmission Using AI locally improves mobility
by reducing reliance on inconsistent wireless networks resulting from dead zones or service outages. Edge AI also lowers communication cost by transmitting less data. However, bringing AI to the edge is no easy task. Design challenges such as performance size, weight, and power
can be overwhelming, but not insurmountable. The secret is heterogenous computing. Heterogeneous computing employs
two or more different types of computing cores, such as CPU, GPU, FPGA, or ASIC that each excels a specific tasks. As opposed to homogeneous computing, heterogeneous computing assigns workloads to the best suited cores. This way, AI is delivered faster in smaller sizes
and with less power consumption. To this end, ADLINK offers
optimized heterogeneous computing platforms that provide a mix of core types for better ROI. ADLINK’s deep learning profiling service helps determine computing cores that give the best performance per dollar and performance per watt. To ensure performance at the system level, ADLINK diagnoses hardware and
software bottlenecks that, when remedied, can greatly increase system responsiveness. ADLINK heterogeneous computing platforms include GPU- and VPU-accelerated boards, systems, and servers to help optimize system architecture for both AI inferencing and training applications. System architects can leverage ADLINK scalable solutions, configure ADLINK platforms according to their AI application needs, and reduce development effort. Take advantage of ADLINK’s edge computing solutions
to optimize the performance and ROI for edge AI.

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