# Recent Developments in Low-Power AI Accelerators: A Survey
## 4 Results
### Summary of Company Accelerators  
This section is dedicated to accelerators where the authors are affiliated with a company. In Table 6, we have gathered all accelerators by different companies. One thing to notice in the table is the relatively high power of the company affiliated accelerators compared to the other accelerators discussed in this survey. With a mean power of 2.9 W and a median power of 1.9 W, the company affiliated accelerators have a 2.2\(x\) increase in mean power and 9.5\(x\) increase in median power compared to non-company affiliated accelerators (1.3 W and 0.2 W, respectively, for non-company affiliated accelerators). This could indicate that the power used in real hardware are higher than what the simulated systems usually predict. Below, we go through some selected company affiliated AI accelerators in more detail. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Company** & **Accelerator** & **Year** & **Tech** & **Area** & **Power** & **Freq.** & **Perf.** & **Power Eff.** & **Area Eff.** & **Type** \\ \hline  & & & **nm** & **mm\({}^{2}\)** & **mW** & **MHz** & **GOPS** & **GOPS/W** & **GOPS/mm\({}^{2}\)** & \\ \hline Samsung & NUP’19 [66] & 2019 & 8 & 5.50 & 796.00 & 933.0 & 4423.5 & 8000.0 & 804.3 * & ASIC \\ Gyrfalcon \({}^{\dagger}\) & Lightspeeur 5801S [59] & 2019 & 28 & 36.00 & 224.00 & 200.0 & 2800.0 & 12,600.0 & 77.8 * & ASIC \\ Gyrfalcon \({}^{\dagger}\) & Lightspeeur 2801S [60] & 2019 & - & 49.00 & 600.00 & 100.0 & 5600.0 & 9300.0 & 114.3 * & ASIC \\ Hailo & Hailo-8 [67] & 2019 & - & 225.00 & 2500.00 & - & 26,000.0 & 10,400.0 * & 115.6 * & ASIC \\ Kneron & KL520 [57] & 2019 & - & - & 575.00 * & 300.00 & 345.0 & 600.0 & - & ASIC \\ NVIDIA & Jetson Nano [33] & 2019 & - & 3150.00 & 7500.00 & - & 250.00 & 37.5 * & 0.1 * & ASIC \\ IBM & Oh et al. [56] & 2020 & 14 & 9.80 & 2727.00 * & 1500.0 & 3000.0 & 1100.0 & 300.0 & ASIC \\ ARM & ETHOS-US55 [52] & 2020 & 16 & 0.10 & - & 1000.0 & 512.0 & - & 2880.0 * & ASIC \\ ARM & ETHOS-U65 [68] & 2020 & 16 & 0.60 & - & - & 1000.0 & - & 1666.7 * & ASIC \\ Kneron & KL720 [58] & 2020 & - & - & 1362.50 & 696.0 & 1500.0 & 1250.0 * & - & ASIC \\ Cambricon & Cambricon-Q [69] & 2021 & 45 & 9.18 * & 1030.31 & 1000.0 & 2000.0 & 1941.2 * & 217.9 * & simulated \\ IBM & RaPiD [70] & 2021 & 7 & 36.00 & 6206.00 * & 1600.0 & 51,200.0 & 8250.0 & 2610.0 & ASIC \\ Samsung & NUP’21 [72,73] & 2021 & 5 & 5.46 & 327.00 & 1196.0 & 14,700.0 & 13,600.0 & 4474.2 * & simulated \\ ARM & Ethos-N78 [74] & 2021 & - & - & - & - & 1000.0 & - & - & ASIC \\ Kneron & KL530 [75] & 2021 & - & - & 500.00 & - & 1000.0 & 2000.0 * & - & ASIC \\ quadric & 2021 & 16 & 256.00 & 5500.00 & 1000.0 & 64,000.0 & 11,636.4 * & 253.9 & ASIC \\ Mythic & MI076 AMP [77] & 2021 & - & 294.50 & 4000.0 & - & 25,000.0 & 6250.0 * & 84.9 & ASIC \\ Xilimx & V22002 [78] & 2021 & 7 & - & 6000.00 & - & 5000.0 & 833.3 * & - & ASIC \\ Xilimx & VE2102 [78] & 2021 & 7 & - & 7000.00 & - & 8000.0 & 1142.9 * & - & ASIC \\ \hline \hline \end{tabular}  
* \({}^{*}\) Calculated based on configuration and/or published metrics of specified accelerator. \({}^{\dagger}\) Gyrfalcon Technology Inc.  
\end{table} 
Table 6: Low-power AI accelerators by companies. RaPID [70] is a server/data center AI accelerator for ultra-low precision training and inference proposed by IBM researchers. RaPID accelerates CNNs, LSTMs, and Transformers. According to the authors, a single-core includes all necessary features to be used as a single-core edge accelerator. Thus, RaPID are designed for dual use in both data centers and edge devices. NPU'21 [72] is a commercial neural processing unit (NPU) architecture for Samsung's flagship mobile system-on-chip (SoC). Using an energy-efficient inner product engine that utilizes the input feature map sparsity in combination with a re-configurable MAC array, a single NPU core achieves a throughput of 290.7 frames/s and 13.6 TOPS/W when executing an 8-bit quantized Inception-v3 model.