# Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World
## 2 Definitions and Motivations  
The number of IoT devices is constantly increasing for several different reasons: low production cost, pervasive electronics, and technology in daily life, availability of wireless and wired communication networks, etc.  
Since these devices are spreading all over, ethic and practical questions had been posed related to energy footprint and sustainability. Indeed, IoT systems are often battery powered devices able to acquire and send data via wireless transmission. This means, from an architectural point of view, that these devices must be energy efficient, thus requiring low power solutions.  
In order to meet all these constraints, IoT systems must be composed of low power sensors, such as MicroElectroMechanical Systems (MEMS), while data are elaborated by means of low power MicroController Units (MCU) and then transmitted. Generally, data are sent as once-in-a-while packets, by exploiting low energy radio transmitters/receivers based on new technologies, such as the UWB communication.  
Since transmission is the most power consuming task among the three steps described above, many techniques have been developed in the past years to make it as low-power as possible. One of the most promising techniques, compressive sensing (described in Section 3.7), represents a very efficient solution, which allows for directly reducing the amount of data collected by sensors. As a consequence, the power needed to transfer data to the cloud for post-processing is much smaller than conventional Nyquist-rate sampling. The energy budget for IoT nodes usually consists of small battery sources or energy harvesters; thus, the ability to reduce the amount of data to be transmitted (and consequently the power) plays a key role in today's systems. Alternative approaches have been proposed in the literature to reduce the rate of data to be transmitted, by relying on bio-inspired techniques.  
Many modern MCU architectures cannot deal with the power budget imposed by IoT nodes, but, even in this strict scenario, researchers developed a broad spectrum of energy saving approaches, such as drastic voltage and frequency scaling. Near-threshold operation of embedded transistors introduces limitations such as performance degradations but still, for certain applications, it represents a very promising solution. Sometimes, for some particular cases, a dedicated hardware approach such as Application Specific Integrated Circuits (ASICs) Figure 2 could be preferred in order to avoid the power consumption of the general purpose MCUs. The main drawback is the loss of flexibility of the architectures. This is overcome by the use of semi-specific processors called Application Specific Integrated Processors (ASIPs [16]) (Figure 2), which is designed so that recurrent application-specific operations (for example, convolution or bit-wise operations) are accelerated with dedicated hardware. The overall processor is not as specifically tailored on the whole application as in ASICs in order to remain flexible and future-ready.  
This approach leads to processors which usually require higher power than ASICs but are able to adapt to the evolution of standards and technologies without re-designing and producing a new integrated circuit, whose cost is a major limitation for the IoT. Ideally, IoT hardware should be at the same time optimized but not too application-specific in order to cut the non-recurrent manufacturing expenses by producing millions of small and cheap integrated circuits. This is a very difficult task, as explained in the following sections. These constraints drive IoT manufacturers towards older silicon technology processes, from 200 nm to 130 nm. These particular nodes are very effective in this field as they have been proven to be mature, cheap, mixed-signal capable, embedded-flash capable, low power and, thus, they are considered a low-risk option. Smaller technological nodes are still needed for high-performance applications, where very fast execution is required.