# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 3 Key Considerations of Object Recognition on Edge Computing
### Deep Learning Frameworks for Edge Implementation  
Deep learning frameworks play a significant role in enabling edge AI for object recognition applications. These frameworks are designed to optimize the deployment of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **GPU [49]** & **TPU [50]** & **FPGA [51]** \\ \hline Flexibility & High—general purpose computing tasks & Moderate—not well suited for other tasks outside AI & Very high—highly customizable at hardware level \\ \hline Performance & High—good for training and inference & High—optimized for machine learning inference & Moderate to High—Can achieve high performance if optimized correctly \\ \hline Ease of use & High—Easy with frameworks & High—Easy with frameworks & Low—requires hardware design expertise \\ \hline Power efficiency & Moderate—Can consume significant power & High—Designed for low power consumption & Very high—Can be optimized for low power consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 1: TPU vs. GPU vs. FPGA-based devices. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Devices** & **Processor** & **Hardware Accelerator** & **Computer Vision** & **Remarks** \\ \hline NVIDIA Jetson & Quad-core ARM & NVIDIA Maxwell GPU & TensorFlow, \\ Nano [52] & Cortex-A57 & (128 CUDA cores) & PyTorch, TensorRT & Affordable GPU \\ \hline NVIDIA Jetson & 6-core ARM & NVIDIA Maxwell GPU & TensorFlow, \\ Orin Nano [53] & Cortex-A78 AE & (128 CUDA cores) & PyTorch, TensorRT & High-performance GPU \\ \hline Google Coral Dev & Quad-core ARM & Edge TPU & TensorFlow Lite, \\ board [54] & Cortex-A53 & & Edge TPU Runtime & TensorFlow models \\ \hline Raspberry Pi & Quad-core ARM & & TensorFlow Lite, \\
4 [55] & Cortex-A72 & No built-in accelerator & Onnx & No dedicated AI \\ \hline Xilinx Kria KV & Quad-core ARM & Xilinx FPGA (Zynq & \\
260 [56] & Cortex-A53 & Ultrascale+) & & \\ \hline Flight RB5 5G & Qualcomm \& Kryo & Qualcomm \& Adreno 650 & Qualcomm Neural & Ideal for real-time \\ Qualcomm UAV & 585 & & Processing SDK & applications using UAV \\ \hline Samsung Galaxy & Qualcomm & Qualcomm Snapdragon & Qualcomm Neural & Ideal for real-time \\ S24 Ultra [57] & Snapdragon & Adreno 750 & Processing SDK, & applications using \\  & octa-core & & TensorFlow Lite & mobile phone \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selection of edge devices. deep learning models in resource-constrained edge devices, ensuring real-time inference and efficient implementation on resource-constrained edge devices. One of the well-known deep learning frameworks on edge devices is TensorFlow Lite. It supports model quantization for efficient deployment that can convert the model to lower-precision formats, such as floating point 16 bits (FP16) or integer 8 bits (INT8), to reduce memory usage and speed up inference [58]. Two types of quantization methods can be supported by TensorFlow Lite, which are post-training quantization and quantization-aware training (QAT) [59]. Post-quantization is a model optimization technique applied after training, where model weights and activations are reduced in precision (e.g., from FP32 to INT8) to improve inference efficiency; it is more straightforward but may lead to slight accuracy degradation. On the other hand, QAT simulates quantization during training by incorporating quantization effects into the loss function, resulting in a model that is more robust to precision reduction and retains higher accuracy after quantization. For hardware acceleration, TensorFlow Lite can support layer fusion, fusing operations, e.g., convolution, activation, and batch normalization, into single kernels to minimize memory transfers and computation overhead. It also has the capability to offload computations to hardware accelerators, such as GPU or TPU, for real-time inference. TensorFlow Lite is ideal for TensorFlow-trained models and specific edge platforms, such as Google Coral and mobile devices. TensorRT from NVIDIA is also another framework that can support model quantization and layer fusion [9].