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The Consortium is now witnessing the emergence of Class B instruments. These LXI Devices allow users to build quick and easy-to-manage test systems that can be remotely controlled, as shown in Figure 1. Web services are used to gain access to LXI Devices and achieve immediate control over their configuration, making them easy to set up, configure, and debug. Some of these Class C instruments include features associated with Class B or Class A LXI Devices but not the complete set of features required to declare class compliance. All major instrument functionalities are accessible through the Web server, which simplifies software development, system commissioning, troubleshooting, and maintenance. These instruments incorporate the key features attributed to LXI-compliant instruments, built-in Web servers so that users can monitor and control the system using Ethernet, and any standard Web browser. Since the release of the LXI standard, most instruments have been certified as Class C. The trigger functionality is broadly equivalent to the backplane triggers of modular instruments in card cages (though cable lengths might be longer than backplane trigger lengths) and the ad hoc point-to-point trigger systems used on bench instruments. The wired trigger bus provides a standardized capability for supporting trigger events between devices whose timing accuracy is limited by cables and LXI Device hardware. The Class B interface allows devices to execute triggered functions equivalent to those available over the General-Purpose Interface Bus (GPIB) with similar or better timing accuracy.įunctional Class A LXI Devices provide a standardized LAN interface, synchronization API, IEEE 1588 operation, and a wired trigger bus interface. This class includes physically small products (such as sensors) that use battery power or Power over Ethernet and devices with key attributes including a simple architecture, low cost, and small size.įunctional Class B LXI Devices provide a standardized LAN interface, synchronization API, and IEEE 1588 timing support. These devices, which are not required to support either the wired trigger or IEEE 1588 timing aspects, are particularly suited to applications where non-LXI products have been adapted to the standard. Classes of LXI productsįunctional Class C LXI Devices provide a standardized LAN and Web browser interface conformant with the LXI standard. The class succession from C to A provides progressively more functionality and gives instrument designers the ability to incorporate the precise functionality required for their instruments and intended applications. To serve eventually IT community with other tangible solutions, we proposed two roadmaps opening up to the possibility of investigating sustainable actions covering hardware, software, and data levels.The LXI standard identifies three functional certification classes: Class C, Class B, and Class A.
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These endeavors were mainly manifested in the form of efficient reconfigurable accelerators designs based on Processing-In-Memory and Processing-Near-Memory architectures. Hereafter, we devoted in accordance with both inquiry and literature results some essential parts for analyzing emerging efficient DNNs and distributed BD implementations. Accordingly, we provided a useful guideline of various multi-level energy and power estimation approaches. Even worse, multi-level energy consumption measurement techniques were not evident to most respondents. Unfortunately, only 10% of respondents were adopting energy metrics when evaluating their implementations. Through an analysis of responses to the first inquiry questions, we were able to address the residing interoperability between AI models, distributed BD frameworks, and cloud-fog systems. This paper conducts a newsworthy inquiry purposing to study at which extent IT community are conscious about energy evaluation of their hardware and software implementations, and whether they are in line with sustainable implications toward efficient AI and BD deployments. Cloud-fog based industries are entailing today greedy energy costs, given the wide multiplication of their AI models and distributed BD frameworks implementations.