## 4 Study and Evaluation
### Hardware Platforms  
In the last few years, a plethora of new cutting-edge mobile and embedded devices have appeared in the market as potential AI-supporting hardware platforms suited for the implementation and continuous improvement of on-device DL models due to their ever-increasing computational power and modest energy consumption compared to the more traditional server or desktop alternatives. In particular, mobile SoCs (System on a Chip), adopted in today's mass-produced embedded devices, such as mainstream single-board computers, have been and still are, definitely, one of the main drivers of the recent on-device trend. Their physical design based on the tight integration of computing, memory, and communication components in a single integrated circuit not only optimizes the internal communication among components and maximizes energy efficiency but also minimizes chips' waste heat, as well as their die area, enabling the miniaturization of the devices built on its basis. Table 4, in particular, the field tagged as \"Test device\", lists the several devices used in the AmI research analyzed for evaluation purposes. It is possible to classify those devices into three different groups: high-performance single-board computers [49; 52; 53; 54; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 75; 76; 77; 82; 83; 89; 91; 92; 93; 94; 105; 106; 108], low-power single-board computers [60; 67; 69; 72; 75; 78; 95; 100; 103], and mobile devices [63; 68; 73; 74; 80; 87; 99]. This categorization, however, does not contemplate papers where the hardware setup used for model deployment is not properly detailed or detailed at all [48; 51; 55; 64; 81; 84; 88; 90; 96; 97; 98; 101; 102; 104; 107], nor the ones that report using desktop systems for that purpose [58; 59; 61; 62; 79; 85; 86; 109]. Moreover, in addition to those omissions, there are two punctual investigations also excluded from the previous classification where authors propose detectors specially designed for specific-purpose hardware: humanoid robots programmed to play soccer [50], and a mixed reality headset used to overlay virtual information [110] on the real world. The group of high-performance single-board computers includes low-energy embedded devices designed specifically for accelerating ML applications through a dedicated built-in processor. As the data presented in Table 4 reflect, this category is monopolized by Nvidia's Jetson family. Up to 23 of the 47 papers detailing the evaluation hardware setup exploited report using at least one device belonging to the Jetson hardware platform for AI processing on edge, being the latter a distributed computation paradigm based on an intermediate layer of resource-constrained devices located physically closer to data source where computation is partially offloaded, thus mitigating latency and reducing response times comparing to the cloud alternative. In particular, the specific devices observed in this respect, listed in ascending order according to their computational power, are: Nvidia Jetson Nano [65; 91; 93; 103], Nvidia Jetson TX1 [52; 70], Nvidia Jetson TX2 [49; 53; 54; 56; 71; 76; 77; 82; 83; 89; 93; 94] and Nvidia Jetson AGXavier [92; 103; 105; 106]. All of them are based on CPUs with ARM architecture, ranging from four cores in the Jetson Nano and Jetson TX series to eight cores in the Nvidia Jetson AGX Xavier. However, their actual AI processing capability lies in the powerful GPUs they embed, delivering performance equivalent to desktop graphics chipsets of recent past years. Their physical design, based on a fair number of cores (128 in the Jetson Nano, 256 in the Jetson TX1 and Jetson TX2, and 512 in the Jetson AGX Xavier), makes it possible to parallelize repetitive operations and thus perform matrix computation more efficiently than general-purpose CPUs. They present, however, more limited memory than their desktop counterparts, insufficient for unrolling and converting convolution operations into matrix operations.