Enhancing AI Inference through Sparsity Support and Transformer Optimization for Minimizing Latency - BlogSeptember 16, 2021
AI models have become more complicated in recent times due to the escalating demand for real-time AI applications in various industries. This necessitates the deployment of high-performing, cutting-edge inference systems in an optimal way. TensorRT's latest version addresses these issues by bringing in additional capabilities to provide more enhanced and responsive conversational AI applications to their customers.
With advancements in cloud computing technology, the architectural memory arrangement of data centers has evolved significantly. To enhance the compute capacity and extensive data processing, there is a need to integrate accelerators that excel at processing specific workloads. Although these devices already connect over PCI Express, the optimization can further improve with the compute express link as it allows heterogeneous processing with various system components.
CMSIS 5.8.0 Optimizes the Neural Networks and Signal Processing for Machine Learning Applications - BlogAugust 17, 2021
Interfacing compatibility determines the scope of applications for hardware devices and peripherals. CMSIS is one such interfacing standard that allows the integration of software entities from multiple vendors. The recent release of Keil’s MDK-ARM 5.35 highlights the specific update of CMSIS from 5.7.0 to 5.8.0 which is the major component of the development kit.
Eta Compute’s Low-Power Architecture Offers Long-Lasting AI Vision Workloads on a Single Battery - BlogAugust 12, 2021
Designing low-power architectures with intensive computational capacities decides the feasibility of machine learning applications. It comes with the challenge of accommodating high processing power and a more extended battery without compromising the compact form factor. So, Eta Compute’s design technology supports developers with limitless application options while consuming minimal power along with the complete assistance of the Edge Impulse machine learning development platform.
Bringing Down the Duration of Hardware Design Life Cycle for Compact High Performing Architecture - BlogAugust 03, 2021
Hardware needs to be compact for ASICs (Application Specific Integrated Chips) with complex architecture. Alongside, the demand for quick and energy efficient functionalities have shaped the circuit design conventions over time. Sondrel's architecture and design have reduced hardware design life cycles, thus lowering cost and time for deployment. It also accommodates the risk to bespoke technology requirements, ensuring the chip expectations are met from the commercial and technical point of view.
Updates and corrections in the minor version of the development kit propel the overall firmware to a stable build for a flawless deployment. The architecture design of the microcontrollers, along with the compatible software support brings out the optimal performance of the hardware.
Windows 10 IoT Enterprise LTSC 2021 offers much more features than the previous version of Windows IoT Enterprise. The new features focus on reducing the net cost and enhancing the connectivity and compatibility of the overall IoT ecosystem.
MicroMod Cards from SparkFun are one of the significant modular interfacing ecosystems. The ecosystem comes with a provision of interchangeable processors and carrier boards, thus allowing rapid prototypes and development for dynamic changes in the projects. The latest MicroMod Teensy processor board enhances the MicroMod applications featuring its Arm Cortex-M7 core.
The hardware components that drive smart vision applications go through a lot of changes during their development lifecycle.
Raspberry Pi SBCs are some of today's most popular development board options. Raspberry Pi comes in flavors ranging from the Raspberry Pi 2, 3, and 4 to the Raspberry Pi Zero and Zero W "maker" boards. Now, the Raspberry Pi Foundation has gone a step further by releasing of its very own processor: The RP2040 MCU.
You may have come across various hardware development boards and MCUs, which use Edge AI for processing and computations. However, cloud computing is still a preferred choice for deployment as it is faster and easier to design applications through the cloud platform. But it comes at the cost of latency in data transfer and security issues as the system is prone to network attacks. Hence, Edge AI addresses these issues as it works on the principle of on-device processing to make the system quick and safe.
Testing and debugging has always been a tedious phase for engineers in the hardware design life cycle. Finding the issues and backtracking errors consumes a lot of productive time which causes the delay in production. Hence, the concept of capturing and monitoring the electronic signals using a Logic Analyzer solves this issue for a Test Engineer to analyze the hardware design.
Processing of human-generated textual data has always been an important yet challenging task, as human language tends to be naturally robust for machines to understand.
XaLogics’s AI Accelerator with K210 SoC comes with a dual-core RISC-V AI processor featuring low power consumption than its competing Coral USB Accelerator, and Intel Neural Compute Stick 2.
The AIoT market needs some help in terms of processing, security, and software support. In order to gain the assistance that’s needed, semiconductor solutions need to step up and meet five key demands: