Adaptive Platforms
Any kind of product or solution with adaptive hardware at its heart is referred to as an adaptive platforms. From the perspective of application development, a platform is something that offers a collection of features that can be used to create a product. It offers developers a strong foundation around which to construct their apps. A platform enables developers to concentrate on the unique uniqueness of their application by offering the foundation for it.
Although they all share a basic adaptive hardware core, adaptive platforms encompass much more than simply the silicon device or hardware. A wide range of design and runtime software is included in adaptive platforms. The technology and software work together to provide a special capacity that may be used to create incredibly adaptable but effective applications.
Advantages of Adaptive Platforms
A wide variety of software and system developers can now utilize adaptive computing to adaptable platforms. Numerous products can be built on top of these platforms. Using an adaptive platform has the following advantages:
Shortened time to market: Without requiring any hardware customization, an application developed on a platform like the Alveo data centre accelerator card can take advantage of accelerated hardware for a particular purpose. Accelerated libraries are called straight from an existing software program after a PCIe card is introduced to the server.
Lower operating expenses: Because of advancements in compute density, optimized applications built on an adaptive platform can offer noticeably higher efficiency per node than CPU-based solutions.
Dynamic and adaptable workloads: Adaptive platforms can be changed to suit the demands of the moment. Using the same hardware, developers can quickly replace the apps installed on an adaptive platform to accommodate shifting workload requirements.
Proven for the future: It is possible to continuously modify adaptive platforms. The lifespan of the system can be increased by reprogramming the hardware to best integrate new features in an existing application. This eliminates the requirement for hardware updates.
Speeding up the entire application: AI inference rarely occurs in a vacuum. It is a component of a longer chain of data processing and analysis, frequently involving several pre- and post-stages that employ conventional (non-AI) implementation. AI acceleration helps these systems’ embedded AI components.
Acceleration also helps the non-AI components. Both AI and non-AI processing activities can be accelerated to adaptive computing’s adaptable nature. This is known as “whole-application acceleration,” and as more apps use compute-intensive AI inference, its significance has grown.
Adaptive Platforms Accessibility
To take advantage of FPGA technology in the past, developers had to construct their own hardware boards and configure the FPGA using a hardware description language (HDL). On the other hand, adaptable platforms let programmers use their favorite software frameworks and languages like C++, Python, TensorFlow, etc. to directly profit from adaptive computing. Adaptive computing is now accessible to software and AI developers without requiring them to be hardware specialists or create a board.
For instance, adaptive computing can be used straight from industry-standard frameworks like FFmpeg by developers of video applications. They can use cutting-edge, effective apps without having to be specialists in FPGAs or hardware-implemented video codecs. Additionally, with simple API call modifications, they can transfer their existing software code to employ adaptive computing.
The usage of adaptive hardware at various levels of design abstraction has been made possible by adaptive platforms. Numerous accelerated APIs are already accessible through vendor-provided, open-source libraries and an independent software vendor (ISV) ecosystem. Additionally, larger design teams can have their own hardware engineers develop unique accelerated APIs that are then used by their software team in a final product.
Adaptive Platform Types
Adaptive platforms come in a variety of forms, such as standardized edge modules and data Centre acceleration cards, depending on the application and requirement. There are several platforms available to provide the ideal foundation for the intended application. Applications range considerably, from latency-sensitive ones like real-time streaming video and autonomous driving to the highly sophisticated 5G signal processing and unstructured database data processing.
By bringing the newest architectural advancements to discrete and end-to-end applications, adaptive computing can be implemented in the cloud, network, edge, and even at the endpoint.
Numerous adaptable platforms, ranging from compact, low-power devices appropriate for endpoint processing required by IoT devices to large-capacity devices on PCIe accelerator cards in the data centre, enable the range of deployment sites.
Kria adaptive system-on-modules (SOMs) from Xilinx are examples of adaptable platforms at the edge. The Zynq UltraScale+ MPSoC architecture from Xilinx serves as the foundation for Kria adaptable SOMs, which provide developers with a readymade adaptive platform for edge applications. Developers can spend more time incorporating features that set their technology apart from the competitors by standardizing the system’s essential components.
The Alveo accelerator card is one of the data center’s adaptive platforms. This enables hardware offloading for any data Centre application using industry-standard PCI-express.
Adaptive computing extends beyond data centre compute offload. Additionally, there are adaptive systems for SmartNIC, where acceleration takes place directly in the network traffic flow, and SmartSSD storage, where acceleration takes place at the storage access point.
