## 5 Hardware Metrics and Comparison  
Comparisons between different hardware platforms or accelerators are not always straightforward as designers often present performance depending on the target application. For example, a GPU for server applications is difficult to compare with an accelerator based on ASIC or FPGA for embedded applications. In fact, the power envelopes and the amount of data to process will be the opposite. However, there are some standard metrics that researchers rely on to define the performance of the hardware that refers mainly to the area, power consumption, and the number of operations per second. **Area. ** The area, generally expressed in squared millimeters or squared micrometers, represents the portion of silicon required to contain all the necessary logic. It strictly depends on the technological node used during the hardware synthesis process and the size of the on-chip memory. **Power. ** The power consumption comes from the device's power envelope and the application for which it was designed. Battery-powered devices, for example, require extremely efficient accelerators that can overcome the pj per MAC barrier. This latter is a metric widely used to express the efficiency of the computational side of architecture. However, power consumption must also include that resulting from both on-chip and off-chip memories as they are the primary source. **Throughput. ** Throughput defines how often an accelerator can accomplish a complete convolution, or rather a complete inference. Throughput and latency are derived from the device's operating frequency coupled with the memory bandwidth. Usually, this metric is expressed as billions of operations per second (Gop/s) or as billions of Macs per second (GMAC/s). Considering that a MAC consists of two operations (multiplication and sum), the ratio between Gop/s and GMAC/s is 2 to 1. There are other metrics that best define an accelerator, such as its flexibility and scalability to new network models or variable bitwidth. As mentioned above, accelerators tend to be very application-dependent, so very often, comparisons between them are complicated and should be evaluated based on datasets or common models. A comparison between many of the aforementioned models is provided in Table 4, where different hardware platforms and different accelerator models are presented. As expected, general purpose architectures have greater area and power consumption than special purpose architectures, as they are not optimized for a specific application. For each architecture, the main hardware metrics discussed above are reported. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Name** & **Platform** & **Reference** & **Technology (nm)** & **Area (mm\({}^{2}\))** & **Power (mW)** & **Gop/s** \\ \hline \hline Cambricon-X & ASIC & [69] & 65 & 6.38 & 954 & 544 \\ \hline SCNN & ASIC & [24] & 16 & 7.9 & - & 2000 \\ \hline EIE & ASIC & [71] & 45 & 40 & 600 & 3000 \\ \hline NullHop & ASIC & [26] & 28 & 8.1 & 155 & 450 \\ \hline NullHop &
\begin{tabular}{c} FPGA \\ Xilinx Zynq 7100 \\ \end{tabular} & [26] & 28 & - & 2300 & 17.2 \\ \hline SqueezeFlow & ASIC & [25] & 65 & 4.80 & 536 & - \\ \hline UNPU & ASIC & [83] & 65 & 16 & 297 & 345–7000 \\ \hline FlexFlow & ASIC & [87] & 65 & 3.89 & 1000 & 420 \\ \hline DNA & ASIC & [88] & 65 & 16 & 479 & 194 \\ \hline SIGMA & ASIC & [93] & 28 & 65.10 & 22,300 & 10,800 \\ \hline DNPU & ASIC & [94] & 65 & 16 & 279 & 300–1200 \\ \hline Nvidia V100 & GPU & [57] & 12 & 815 & 250,000 & 31,400 \\ \hline Nvidia A100 & GPU & [58] & 7 & 826 & 400,000 & 78,000 \\ \hline Intel Xeon & \multirow{2}{*}{CPU} & \multirow{2}{*}{-} & \multirow{2}{*}{14} & \multirow{2}{*}{-} & \multirow{2}{*}{400,000} & \multirow{2}{*}{3200} \\ Platinum 9282 & & & & & \\ \hline AMD Ryzen & \multirow{2}{*}{CPU} & \multirow{2}{*}{-} & \multirow{2}{*}{7} & \multirow{2}{*}{-} & \multirow{2}{*}{280,000} & \multirow{2}{*}{1859} \\ Threadripper 3970x & & & & & & \\ \hline \hline \end{tabular}
\end{table}