# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 3 Computational Platforms
The FPGA trades design complexity for flexibility with microcontrollers; SoC takes the best of both worlds with on-chip microprocessor units, communication, interface, and special processing IPs interfaced with on-chip reconfigurable hardware via a specially designed high-bandwidth, low-latency interface. Xilinx seems to be the leader in this field, with Zynq being most used SoC platform and Infineon PSoC being a popular choice as well. A board-level integration of SoC with highly specialized peripherals for particular application constitutes the next level of specialized hardware, often termed as system on module (SoM); Kria from Xilinx, phyCORE from PHYTEC, and COMxpress from ReFLEX CES are some examples of SoMs for vision, multimedia, machine learning, and embedded applications. Some common SoM solutions are listed in Table 7. Another interesting computing eco-system is commonly termed the single-board computer (SBC); though similar to SoMs in organizational specification, the architectural configuration of SBCs has a general-purpose computing flavor, allowing flexibility and price advantage at the cost of performance, and they are found to be suitable for an extensive range of applications compared to SoMs, which find usage in highly specialized high-performance applications. In fact, SBCs represent the most popular segment for IoT developers and account for most of the designs reported in the scientific literature on the range of topics including smart meters, with Raspberry PI and Arduino as the most popular examples. Reviewing the architectural choices for AI processing on the edge warrants special attention because of the continued evolution of the smart meter functionality into the realm of processing data on the edge involving AI-based operations for various smart energy applications. Edge AI processing hardware can be integrated as special acceleration units in the form of independent processors. The Intel Neural Compute Engine, MediaTek AI processing unit, Google Edge TPU, Gyfralcon Matrix Processing Unit, Mythic analog matrix processor, Syntiant Neural Decision Processors, and Halio AI processors are some examples of the available solutions. FPGA-based solutions are based on specialized soft processor design for AI applications or specialized FPGA architecture intended for AI. Former optimizations include designs of accelerators for object detection [148], the YOLO framework [149], the recurrent neural network (RNN) [150], the long short-term memory network (LSTM) [151], and processors for the edge and IoT [152], whereas the latter include matrix multipliers, pipeline architectures, and DSP slices, etc., such as MAX V CPLD and cyclone FPGAs from Intel. The single-board computers for edge applications include Raspberry Pi 4, Apollo 3 Blue, Hailo 8, Coral Dev, Jetson Nano boards, etc. Some specialized SoCs for smart metering have also been designed. The details of these are provided in Table 7.  
Finally, the advancement in hardware is well supported by the software eco-system in the AI edge development. Real-time operating systems, programming libraries, and tool chains have been developed to ease the research and production of AI and edge solutions for smart systems such as smart energy and components such as the smart metering infrastructure. Popular OSs for the embedded edge are Linux and its variants, Android, Azure, Windows, FreeRTOS, mu-Linux, VxWorks, Mbed, embOS, Lynx, RTX, QNX Neutrino, MIPS Embedded OS, TI-RTOS, etc. ; these are just few of the examples. Tool chains are collections of programs that can use the development system to develop and debug the program and then compile it for the end system and are very popular in the embedded system design domain. The resource-constrained microcontrollers use special boot loaders that solely act to load the user program directly from the development system directly to the microcontroller chip for short-cycle embedded development such as Arduino. Other popular tool chains are based around GCCs, e.g., the ARM tool chain.