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Significant advantages in executing CV tasks on edge devices from Ampere.

AI Computer Vision Workloads on the Edge

How to Meet the High Computational Demands

Ampere Processors Optimally Balance Performance and Power Usage
Features and Compatibility
High-end Compute Demands of AI in Edge Deployments
Future-proof your Enterprise by Deploying CV Workloads on Edge on Ampere Processors
Ampere Processors Optimally Balance Performance and Power Usage

Ampere Computing, with its innovative processor architecture, offers significant advantages in executing CV tasks on edge devices. The benefits become more pronounced when compared to legacy x86 processors, such as Intel's Xeon D series.

  • Performance: Ampere's edge processors are designed for high-performance computing. They offer a high core count and support for multithreading, which enable efficient parallel processing of AI workloads. This is particularly valuable for the simultaneous computations required in CV tasks.

  • Energy Efficiency: Ampere processors are known for their exceptional power efficiency. This is critical for edge devices, where power consumption can limit operational longevity. In comparison to Intel's Xeon D series, Ampere's processors deliver superior performance per watt, maximizing the efficiency of edge devices, which often have to operate in power-restricted environments.

  • Cost-Effectiveness: The combination of high performance and power efficiency makes Ampere processors a cost-effective alternative to Intel's Xeon D products. When total cost of ownership is considered, incorporating the cost of power, cooling, and maintenance, Ampere's edge devices offer a compelling advantage of up to 5.4x vs. Intel based competitor products.

  • Predictability of Performance: Ampere processors deliver consistent and reliable performance. Unlike some processors that may exhibit performance variability due to thermal throttling or other factors, Ampere's processors maintain a steady level of performance, ensuring the dependability required for real-time, mission-critical CV tasks on edge devices. Ampere’s architecture effectively eliminates the "noisy neighbor" effect, where multiple tasks on the same processor interfere with each other's performance. This ensures that each core can effectively run its own task without being affected by others.

  • Scalability: Ampere's edge processors offer scalable solutions to fit a variety of needs. Whether it's a smaller edge device or a larger edge server, Ampere has efficient and powerful processor options that can be customized based on workload requirements.

Features and Compatibility

Ampere processors support a vast ecosystem of AI frameworks and libraries. The open-source nature of this ecosystem fosters flexibility and ease of development, making model deployment and updates quick and straightforward.

Along this line, aiming to provide customers with ease-of-use and full access to some of the best solutions available on the market, Ampere processors natively support NVIDIA GPUs. This integration enables seamless, high-speed data exchange between the CPU and GPU, enhancing the performance of hybrid workloads that utilize both the CPU and GPU.

One of the unique features of the Ampere processors is their native support of the FP16 (16-bit floating point) data format. Many AI workloads, including CV tasks, can utilize FP16 calculations for their operations. Using FP16 can significantly boost performance and reduce memory usage compared to FP32 or FP64 formats. It also lowers the power requirements, making Ampere's processors even more efficient for edge devices.

High-end Compute Demands of AI in Edge Deployments

High-end compute CPUs are crucial in edge deployments due to the intensive computational demands of many AI workloads. By having high compute CPUs on edge devices, these tasks can be executed locally, reducing the need for data transfer to a central server and thereby minimizing latency.

To illustrate the excellent performance of Ampere edge devices we have chosen the YOLOv8 model, the newest iteration of the YOLO (You Only Look Once) computer vision model, which has been instrumental in various industries due to its real-time object detection capability. The benchmarks included showcase the performance of Ampere processors in the 32 (Q32-17), 64 (Q64-22) and the 96 core (M96-28) variants. For the complete list of the Ampere Altra family products visit the Ampere website.

Fig 1. YOLOv8 Latency [ms] (Lower Is Better)

In AI inference, latency signifies the duration needed for a model to process an input and generate an output. This becomes critically important in real-time applications demanding swift decision-making, such as autonomous driving. On the edge, where computational resources are typically constrained, achieving low latency becomes even more critical as it allows timely responses to dynamic environmental conditions.

Fig 2. YOLOv8 Throughput [fps]

Throughput in the context of AI inference relates to the volume of data points that a model can handle within a specific period. This is especially vital in applications that involve the processing of high volumes of data, like managing transactions in e-commerce or evaluating extensive datasets in scientific research. On the edge, high throughput allows for the efficient processing of data locally, reducing the need for continuous communication with a central server, which can be time-consuming and costly. As such, maximizing throughput is a key performance indicator for AI deployments on edge devices, as it enables faster decision-making and more effective use of local computational resources.

Ampere AI provides free access to its open-sourced YOLOv8 demo, which showcases the application of the model in a traffic environment (sample of a recording included below for illustrative purposes). You can test it out yourself and verify the performance. The demo can just as well be run on Ampere data center CPUs, including the instances offered by all major CSPs partnered with Ampere, e.g., Azure, GCP, OCI, Tencent, and Alibaba Cloud. Note that the performance will vary depending on the device used as well as the benchmarking setup. Please reach out to the Ampere AI team with any inquiries, the contact is provided at the end of this brief.

Computer Vision Demo Fig. 3 Computer Vision Demonstration

Future-proof your Enterprise by Deploying CV Workloads on Edge on Ampere Processors

Energy-efficient, high compute CPUs address the unique challenges posed by AI inference on edge devices. They offer the computational power necessary for CV tasks while adhering to the space, weight, and power constraints inherent in edge deployments. This makes them a favorable choice for AI workloads in an edge computing environment.

As the demand for real-time, on-site AI solutions continues to grow, edge computing, and specifically CV tasks at the edge, is poised to play a pivotal role. However, the constraints of edge environments, including space, weight, and power limitations, present unique challenges.

Ampere’s innovative processors have emerged as a compelling answer to these challenges. Offering a potent blend of high performance, energy efficiency, and superior compatibility Ampere processors stand out as a robust platform for executing CV workloads on the edge.

We invite you to explore how Ampere's edge processors unlock the full potential of AI on the edge. Join us in leading the way towards a more connected, intelligent, and efficient future. Discover Ampere - where the edge meets the future of AI.


*The edge computing comparisons here are based on benchmark comparisons measured and published by Ampere Computing.
Details and device level footnotes are available here.

Created At : July 14th 2023, 6:21:25 pm
Last Updated At : July 18th 2023, 3:31:21 pm
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