Accelerators Will Play a Key Role in 5G and Edge Computing
- March 17, 2020
Co-Author: Ramesh Radhakrishnan, Distinguished Engineer, Office of the CTO, Server and Infrastructure Systems at Dell EMC
Over the past several years, there has been a growing interest in the use of accelerators on standard servers to improve workload performance. It started with GPUs to accelerate AI/ML and now includes FPGAs, SMART-NICs on servers and other low-power embedded accelerators in end-devices for data analytics, inferencing and machine learning. In this blog, we share our perspective on these new classes of emerging accelerators and the role they will play in the growing adoption of IoT and 5G as workloads get distributed from edge to data center to cloud.
With the exponential growth of data, an increasing number of IoT devices at the edge, and every industry going through digital transformation, the future of computing is driven by the need to process data cost-effectively, maximize business value and deliver a return on investment. The Data Decade is leading to architectures that process data close to the source of data generation and only send information over long-haul networks that requires storage or higher-level analysis. Emerging use cases around autonomous vehicles (self-driving cars, drones), smart-city projects, and smart factories (robots, mission critical equipment control) require data processing and decision making closer to the point of data generation due to mission critical, low-latency and near-real time requirements of these deployments.
Edge computing architectures are emerging with compute infrastructure and applications distributed across edge to cloud. This trend will lead to a range of compute architectures optimized along different vectors – massively parallel floating-point compute capability in the data center to train complex neural network models (where power is not a concern) to highly power-efficient devices that can infer the deployed neural network models at the edge. This leads to a Cambrian explosion of devices that will be used as part of this cloud-to-edge continuum.On the processor front, in the last ten to fifteen years traditional CPU architectures have evolved to an increasing number of cores and memory, but I/O and memory bandwidth hasn’t kept pace. Scaling memory and I/O bandwidth is critical for processing massive datasets in the data center and real-time streaming at the edge. These factors are leading to the evolution of hardware acceleration in both networking and storage devices to optimize dataflow across CPU, memory and IO subsystems at the overall system level. The growth in data processing has led to use of dedicated accelerators for Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) workloads. These accelerators perform parallel computation and faster execution of AI jobs compared to traditional CPU architectures. They provide dedicated support for efficient execution of matrix math which dominates ML/DL workloads. Multiple numerical precision modes beyond what is available in CPUs (BFLOAT16, Mixed Precision Floating Point) are available to massively speedup a broad spectrum of AI applications.
Below, we take a look at five key areas where accelerators will play a pivotal role. Accelerator technologies play a key role in the rollout of 5G technology and associated services (which include AI, AR/VR and content sharing among others) at every stage of the compute spectrum.
In addition to the above opportunities around accelerators, there is an increasing amount of data being stored on the storage systems. In order to provide intelligent access to data, future storage devices are evolving to be programmable, where FPGA and other hardware acceleration techniques are embedded in the drive subsystem to analyze the data in-place and only provide the result to the application. These drives will have capabilities to run third party code and are commonly referred to as computational storage. It optimizes the data transfer over the network, where large videos and images can be analyzed, and database queries can be performed right where data is stored. Large storage systems are also embedding accelerators, virtualization and cloud-native frameworks in the storage system to process data and host third party analytics applications.
This use of hardware acceleration for machine learning, network services and storage services is just the beginning of a change in system-level architecture. The next evolution will drive more optimized data-flows across various accelerators so that data can flow between network, storage and GPUs directly without involving the host x86 CPUs and host memory. This will become increasingly important in future dis-aggregated and composable server architectures wherein a logical server is composed from independent pools of CPUs, memory, network adapters, disk drives, and GPUs connected with a high-performance fabric.
Research is underway to enhance machine learning accelerators for capabilities like reinforcement learning and explainable AI. Future ML accelerators will support capabilities to enable localized training at the edge to further improve decision making for localized data sets. Accelerators at the edge also need to account for environmental conditions at the deployment location. Many edge deployments need ruggedized infrastructure as it is either deployed in outside environment (street side, parking lot or outside a building) or in a warehouse environment (e.g. retail, factory). The power, thermal and form factor requirements need to be considered to build the ruggedized infrastructure containing accelerators for edge deployments.
The companies that innovate in this system-level architecture for next generation workloads will win in driving customers towards digital transformation, edge and 5G. At Dell EMC, we are heavily focused on this evolving use of accelerators, ruggedized IT infrastructure and system level optimizations. Please see the recent Dell Technologies announcement around PowerEdge XE2420 ruggedized compute platform, Dell EMC Modular Data Center Micro 415 and Dell EMC Streaming Data Platform.
To learn more about PowerEdge servers, visit the PowerEdge Servers page, Dell EMC Accelerator Solutions page, Dell EMC Edge Computing Solutions page or join the conversation on Twitter.