# Recent Developments in Low-Power AI Accelerators: A Survey
## Appendix A  
All accelerators included in this survey are listed in Tables 1-4. Due to size restrictions, some information are omitted.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Accelerator** & **Acc. Target** & **Tech** & **Area** & **Power** & **Freq.** & **Perf.** & **Power Eff.** & **Area Eff.** & **Type** \\ \hline  & & **nm** & **mm\({}^{2}\)** & **mW** & **MHz** & **GOPS** & **GOPS/W** & **GOPS/mm\({}^{2}\)** & \\ \hline  & & **nm** & **mm\({}^{2}\)** & **mW** & **MHz** & **GOPS** & **GOPS/W** & **GOPS/mm\({}^{2}\)** & \\ \hline \hline Eyeriss v2 [47] & CNN & 65 & - & 476.25 \({}^{\star}\) & 200.0 & 153.6 & 578.3 & - & simulated \\ [PERSON] et al. [79] & CNN & - & - & 1770.00 & 160.0 & 41.0 & 23.1 & - & FFGA \\ [PERSON] et al. [31] & CNN & 40 & 2.16 & 68.00 & 50.0 & 3.2 \({}^{\star}\) & 47.1 \({}^{\star}\) & 1.5 \({}^{\star}\) & FFGA \\ SparTen [80] & CNN & 45 & 0.77 & 118.30 & 800.0 & 25.6 \({}^{\star}\) & 216.4 \({}^{\star}\) & 33.4 \({}^{\star}\) & simulated \\ DT-CNN [81] & CNN, GAN & 65 & 6.80 & 196.00 & 200.0 & 639.7 & 3260.0 & 94.1 & simulated \\ Dante [82] & DNN & 14 & 2.30 & - & 330.0 & - & simulated \\ TI [53] & DNN & 28 & 1.74 & 154.80 & 1000.0 & 7640.0 & 72,900.0 & 43,807.3 \({}^{\star}\) & simulated \\ MmFast [84] & Transformer & 28 & - & - & 1000.0 & 120.0 & - & - & FFGA \\ ExTensor [85] & SpGEMM & 32 & 98.89 & - & 1000.0 & 128.0 & - & - & simulated \\ WAX [29] & CNN & 28 & 0.33 & 1.55 \({}^{\star}\) & 200.0 & 26.2 \({}^{\star}\) & 169,985.5 \({}^{\star}\) & 206.0 & simulated \\ CGNet-NM [41] & CNN & 28 & 3.86 & 195.90 & 500.0 & 144.0 \({}^{\star}\) & 902.8 \({}^{\star}\) & 37.3 \({}^{\star}\) & simulated \\ Tianjic [61,62] & SNN, ANN & 28 & 14.44 & 950.00 & 300.0 & 1214.1 \({}^{\star}\) & 1278.0 & 84.1 \({}^{\star}\) & ASIC \\ MASSR [86] & RNN & 16 & 4.70 & 375.00 & 1000.0 & 544.0 \({}^{\star}\) & 1051.4 \({}^{\star}\) & 194.6 \({}^{\star}\) & simulated \\ [PERSON] et al. [87] & CNN & 28 & 10.92 & 154.03 & 240.0 & 604.7 & 2970.0 & 55.4 \({}^{\star}\) & ASIC \\ NPU\({}^{\dagger}\)[96] \({}^{\ddagger}\) & DNN, CNN & 8 & 5.50 & 796.00 & 933.0 & 4423.5 & 8000.0 & 804.3 \({}^{\star}\) & ASIC \\ LNPU [88] & CNN, LSTM, RNN & 45 & 16.00 & 367.00 & 200.0 & 2482.8 \({}^{\star}\) & 3382.5 \({}^{\star}\) & 77.6 \({}^{\star}\) & ASIC \\ eCNN [89] & CNN & 40 & 55.23 & 6950.00 & 250.0 & 41,000.0 & 5899.3 & 742.4 & simulated \\ Lightspeeur 5801S [59] \({}^{\ddagger}\) & CNN & 28 & 36.00 & 224.00 & 200.0 & 2800.0 & 12,600.0 & 77.8 \({}^{\star}\) & ASIC \\ Lightspeeur 2801S [60] \({}^{\ddagger}\) & CNN & - & 49.00 & 600.00 & 100.0 & 5600.0 & 9300.0 & 114.3 \({}^{\star}\) & ASIC \\ Hailo-8 [67] \({}^{\ddagger}\) & DNN & - & 225.00 & 2500.00 & - & 26,00.0 & 1040.0 \({}^{\star}\) & 115.6 \({}^{\star}\) & ASIC \\ KL520 [57] \({}^{\ddagger}\) & LSTM, CNN & - & - & 575.00 \({}^{\star}\) & 300.0 & 345.0 & 600.0 & \({}^{\star}\) & ASIC \\ Jetson Nano [33] \({}^{\ddagger}\) & DNN & - & 3150.00 & 7500.00 & - & 250.0 & 37.5 \({}^{\star}\) & 0.1 \({}^{\star}\) & ASIC \\ \hline \hline \end{tabular} \({}^{\star}\) Calculated based on configuration and/or published metrics of specified accelerator. \({}^{\ddagger}\) Published research paper’s authors are affiliated with a company (see Table 6 for specifics).  
\end{table}
Table 1: Low-power AI accelerators from 2019.