# Recent Developments in Low-Power AI Accelerators: A Survey
## 4 Results
### Neuromorphic Accelerators
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Accelerator** & **Year** & **Tech** & **Area** & **Power** & **Freq.** & **Perf.** & \
\begin{tabular}{c} **Power** \\ **Eff.** \\ \end{tabular} &
\begin{tabular}{c} **Area** \\ **Eff.** \\ \end{tabular} & **Type** & **Acc. Target** \\ \hline  & **nm** & **mm\({}^{\text{2}}\)** & **mW** & **MHz** & **GOPS** & **GOPS/W** & **GOPS/mm\({}^{\text{2}}\)** & \\ \hline Tianjic [61; 62] & 2019 & 28 & 14.44 & 950.0 & 300.0 & 1214.1 * & 1278.0 & 84.1 * & ASIC & SNN, ANN \\ SpinalFlow [48] & 2020 & 28 & 2.09 & 162.4 & 200.0 & 25.6 * & 157.6 * & 32.0 * & simulated & SNN \\ NEBULA [51] & 2020 & 32 & 86.73 & 5200.0 & 1200.0 & - & - & simulated & SNN, ANN \\ YOSO [42] & 2020 & 22 & - & 0.73 & +1.0 & 1.3 * & 1757.8 * & - & simulated & SNN \\ IMPULSE [28] & 2021 & 65 & 0.09 & 0.2 & 200.0 & 0.2 & 990.0 & 2.2 & ASIC & SNN \\ VSA [63] & 2021 & 40 & - & 88.97 & 500.0 & 2304.0 & 25,900.0 & - & simulated & SNN \\ [PERSON] et al. [64] & 2022 & 28 & 0.89 & 149.3 & 500.0 & - & - & simulated & SNN \\ Skydiver [65] & 2022 & - & - & 960.0 & 200.0 & 22.6 * & 19.3 * & - & FPGA & SNN \\ \hline \hline \end{tabular}  
* Calculated based on configuration and/or published metrics of specified accelerator.
* Corresponding research paper display results in GSyOPS (Giga-Synaptic Operations per Second) instead of GOPS.  
\end{table} 
Table 5: Low-power SNN accelerators. YOSO [42] is a scalable and configurable low-power accelerator targeting TTFS-encoded SNNs. Time To First Spike (TTFS) is a common temporal encoding scheme in hardware SNN accelerators, but is usually not used in favor of the traditional rate-based scheme due to their low accuracy compared with the latter. YOSO solves this by introducing a new training algorithm that reduces the approximation error which accumulates as a result of converting ANNs to SNNs, thereby increasing the accuracy. Thus, TTFS-encoded SNNs can be considered for traditional ANN tasks, at higher efficiency and with comparable accuracy (within 0.2% to ANN). In addition, YOSO operates at very low clock frequencies, i.e., <1.0 MHz. IMPULSE [28] is a low-power SNN accelerator, incorporating a SRAM-based compute-in-memory (CIM) macro for all necessary instructions in a SNN, including a fused weight and membrane potential memory. Further, IMPULSE consumes extremely low power, i.e., only 0.2 mW. The proposed SRAM-based CIM allows for a decreased memory access time compared to previous SNN accelerators; membrane potential usually incurs additional memory accesses in traditional SNN hardware. VSA [65] is a configurable low-power SNN accelerator for both inference and training. As shown in Table 5, VSA has the highest power efficiency of the reviewed SNN accelerators. A binary weight spiking model which integrates and fires based on batch normalization is proposed, which allows for small time steps when direct training with the input encoding layer and spatio-temporal back propagation. The previous mentioned model and the support for different inference spike times and multi-bit input to the encoding layer, allowing VSA to have a high power efficiency.