# Recent Developments in Low-Power AI Accelerators: A Survey
## 4 Results
### Number and Precision Formats  
Similarly to the acceleration targets, the number and precision formats used for each accelerator are plotted and presented in Figure 12. For similar reasons as for the acceleration targets, the precision formats are grouped together for clarity. The five categories that the accelerators were grouped into are as follows: _INT_ denotes the usage of integers, _FP_ denotes the usage of floating point formats, _FxP_ denotes fixed-point formats, _BFP_ refers to Google Brain's bfloat format, _Bit_ denotes all accelerators where the number of bits used were mentioned but not the data type (integer, float, fixed-point, etc. ), and _unspecified_ denotes accelerators where the precision format was not explicitly mentioned, nor the type of the format. It should be noted, however, that similar to acceleration targets, accelerators can use more than one precision format. Based on the highest number of accelerators that uses a specific format, it is apparent that _Bit_ is the most common one. As _Bit_ denotes the accelerators that did not specify the type of precision format, we can assume that most, if not all, of these accelerators use integers. This conclusion is based on the fact that most AI accelerators accelerate some kind of neural networks, often models that are quite large. Therefore, integers are often used when accelerating these kind of models, both because it is faster and due to it being  
Figure 11: Number of accelerators and targets for both company and non-company affiliated accelerators over the years. Figure 12: Occurrences of different precision formats throughout the years. Where _unspecified_ denotes all accelerators where the precision format used was not explicitly mentioned, and _Bit_ denotes all accelerators where the number of bits used were mentioned, but not the type (integer, float, fixed-point, etc.). more energy efficient. This hypothesis is backed up by analyzing the results in the survey paper from 2021 by [PERSON] et al. (2021), where they observe that INT8 is the most common precision format, with INT16 and FP16 as close runners-up. Inspecting only accelerators with a power of less than 10 W, it becomes apparent that INT8 and INT16 are the only precision formats used in the multiplication and accumulation for low-power accelerators gathered in surveys by [PERSON] et al. (2021). Following this observation and assuming that our assumption of the _Bit_ format is correct, the integers dominate the field. The second most common format, ignoring the _unspecified_ formats, is the floating point format (_FP_), as seen in Figure 12. According to our results, the use of floating points has not changed much over the years. On the other hand, for _FxP_ and _BFP_, a clear increase in usage over time is observed. Regarding the _FxP_ format, there is a steady increase of its usage in low-power accelerators. An underlying reason is probably the stricter power budget in low-power AI accelerators. Looking at the _BFP_ format, an observation can be made. The popularity of the bfloat increases significantly in 2021, and as bfloat was first announced in 2018, we can assume that the underlying reason for this increase now, instead of before, is due to the transition time into the low-power domain. Presented in Figure 13, the precision formats are grouped together with regard to the number of bits used. Divided into less or equal to 8-bit precision and above 8-bit precision, it is observed that most accelerators use less than or equal to 8-bit precision formats, which is in line with the results from the survey by [PERSON] et al. (2021). One might reason that the trend is that more accelerators tend to use \(\leq\)8-bit precision formats over \(>\)8-bit precision formats over time, e.g., as a result of power constraints. However, we have observed that \(\leq\)8-bit precision formats are often complemented by \(>\)8-bit precision formats, or at least 16-bit precision formats. Looking at Figure 13, there is a large increase in higher precision formats in 2021 (more bits).