Intel oneAPI Base & IoT Toolkit

The Intel oneAPI Base & IoT Toolkit is a solution designed to build, analyze, and optimize data-centric IoT applications and solutions that run at the network's edge. According to the vendor, the toolkit caters to companies of all sizes, ranging from small startups to large enterprises. It is intended for professionals in software development, IoT solution providers, system integrators, data scientists, and embedded systems engineers who aim to accelerate their development process and improve the performance and efficiency of their IoT applications.

Key Features

Intel® oneAPI Collective Communications Library: According to the vendor, this feature allows users to implement optimized communication patterns to distribute deep learning model training across multiple nodes. It is said to enhance performance and scalability of distributed deep learning applications, as well as improve efficiency and reduce communication overhead in data parallel workloads.

Advanced Vector Extensions (AVX-512) support: The vendor claims that this feature enables users to utilize the AVX-512 instruction set to accelerate vectorized computations and improve performance in data-intensive workloads. It is said to achieve higher throughput and efficiency for demanding IoT applications.

Intel® VTune™ Profiler: According to the vendor, this feature allows users to analyze and optimize the performance of IoT applications by identifying hotspots, bottlenecks, and areas for improvement. It is claimed to provide valuable insights into CPU, GPU, and memory usage, thereby enhancing overall system performance.

Intel® Distribution of OpenVINO™ toolkit: The vendor states that this feature enables users to deploy and optimize deep learning models on various IoT devices, including CPUs, GPUs, and FPGAs. It is said to improve inference performance and reduce latency for real-time applications at the edge.

Intel® Threading Building Blocks (TBB): According to the vendor, this feature allows users to develop scalable and parallel applications for IoT systems using TBB's task-based programming model. It is claimed to utilize efficient task scheduling and load balancing to fully utilize the available computing resources.

Intel® Math Kernel Library (MKL): The vendor claims that this feature enables users to accelerate mathematical computations and linear algebra operations in IoT applications. It is said to leverage optimized routines for improved performance and efficiency, enabling faster data processing and analysis.

Intel® Integrated Performance Primitives (IPP): According to the vendor, this feature provides users with access to a comprehensive library of optimized functions for image, signal, and data processing tasks. It is claimed to improve the performance and accuracy of image recognition, audio processing, and data compression in IoT applications.

Intel® System Studio: The vendor states that this feature allows users to develop, debug, and optimize IoT applications using a comprehensive set of tools and libraries. It is claimed to provide integrated development environments, compilers, debuggers, and performance analyzers to streamline the development process.

Intel® Distribution for Python: According to the vendor, this feature enables users to leverage optimized Python libraries to accelerate data analysis, machine learning, and deep learning tasks in IoT applications. It is said to improve productivity and performance in Python-based development workflows.

Intel® FPGA OpenCL™ SDK: The vendor claims that this feature allows users to develop and deploy FPGA-accelerated IoT applications using OpenCL, a high-level programming language. It is said to utilize the parallel computing capabilities of FPGAs to accelerate data processing and achieve low-latency responses at the edge.