# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 3 Key Considerations of Object Recognition on Edge Computing
### Hardware Optimization  
Hardware optimization is crucial for implementing edge AI, given the resource constraints of edge devices. There are some well-known hardware optimization techniques, as defined in Table 6. Table 6 also cites the references and highlights the achievements in the research. Pipeline design divides tasks into sequential stages that can execute concurrently, maximizing throughput and reducing latency for real-time inference [83]. Parallel processing leverages multiple hardware resources to perform operations simultaneously, significantly improving inference speed but requiring careful synchronization [84]. Dataflow optimization focuses on minimizing data transfer delays by optimizing memory \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Key Features** & **Advantages** & **Limitations** & **Common Use Cases** \\ \hline Model quantization [78] & Reduces the precision of weights and activations (e.g., from 32-bit floating-point to 8-bit integer). & Reduces memory usage, accelerates inference, and lowers power consumption. & May cause slight accuracy degradation. & Battery-powered edge devices, real-time systems. \\ \hline Pruning and Sparsity [79] & Removes unnecessary weights or neurons from the model. & Reduces model size and speeds up computation. & Requires retraining or fine-tuning to minimize accuracy loss. & Lightweight edge models, IoT devices. \\ \hline Knowledge Distillation [80] & Transfers knowledge from a large, complex model (teacher) to a smaller, efficient model (student). & Fits large models into edge hardware constraints. & Requires careful tuning to balance performance and accuracy. & Edge inference in low-memory devices. \\ \hline Low-rank factorization [81] & Decomposes large-weight matrices into smaller, lower-rank components. & Reduces the number of parameters and computations. & May reduce model capacity, leading to accuracy degradation on complex data. & Efficient object recognition in video analytics and edge inference. \\ \hline Layer Fusion [82] & Combines multiple operations into a single layer to reduce redundant computation. & Lowers inference latency and increases efficiency. & Limited to compatible operations and frameworks. & Optimized edge AI pipelines. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model compression techniques. access patterns, which enhances processing efficiency, particularly in data-heavy tasks [85]. Winograd convolution accelerates convolutional layers by reducing the number of multiplications, making it ideal for optimizing CNN-based object recognition models [86]. Memory hierarchy optimization customizes on-chip memory usage to minimize reliance on slower off-chip memory, improving data locality and reducing access latency [87]. Approximate computing minorly sacrifices accuracy for faster, lower-power operations by reducing computation precision or complexity, making it suitable for energy-constrained devices like drones and IoT cameras [88; 89]. These techniques, when combined, enable real-time, scalable AI deployment on edge devices with limited resources. For example, [PERSON] et al. [83], and [PERSON] et al. [84] achieved a throughput of 24,715 GOP and 3458 Gbps for a proposing pipeline and parallel processing techniques, respectively. In another work, [PERSON] et al. achieved an average reduction of 49% in energy consumption for their proposed tunable compressor, and an average reduction of 36% in energy consumption for the proposed multiply and accumulate (MAC) unit [89]. However, each optimization requires careful tuning to balance accuracy, speed, and hardware constraints. Another approach in hardware design for edge AI is hardware-software (HW-SW) co-design and co-optimization [90]. HW-SW co-design and co-optimization is a development approach that integrates both hardware and software optimizations to create efficient edge AI solutions for object recognition on FPGAs.