JetBlock: Efficient Embedded Segmentation

• JetBlock is an architectural unit designed for real-time semantic segmentation, balancing inference speed, memory usage, and feature abstraction through group convolutions, channel shuffle, and JetConv attention.
• Its design minimizes computational complexity and parameter count by employing lightweight activations, grouped pointwise convolutions, and residual connections for effective feature fusion.
• JetBlock significantly reduces model parameters and GFLOPs in the JetSeg model, enabling up to 2x faster inference on low-power, GPU-embedded platforms like NVIDIA Jetson devices.

JetBlock is an efficient architectural unit embedded within the JetNet encoder of the JetSeg model, devised to optimize real-time semantic segmentation on low‐power GPU-embedded systems. The central objective of JetBlock is to balance inference speed, memory usage, and feature abstraction capability, enabling deployment in resource-constrained environments such as NVIDIA Jetson devices while maintaining high segmentation accuracy. JetBlock’s structure systematically minimizes parameter count and inference time through a coordinated sequence of group convolutions, channel shuffle, advanced feature extraction via JetConv, attention mechanisms, and residual connections—achieving notable parameter and computational reductions relative to prior models.

1. Structural Composition and Sequential Operations

JetBlock is organized into sequential processing stages designed to maximize computational efficiency and representation power:

1. Group Convolutions with Normalization and Lightweight Activation: Input channels are partitioned into discrete groups, and convolutions are performed independently within each group, which substantially lowers the parameter count and mitigates overfitting effects. The output is stabilized with batch normalization (BN) and transformed by the lightweight activation function TanhExp, allowing nonlinearity at a reduced inference cost.

$$Y_g = \mathrm{BN}(\mathrm{Conv}_g(X)),\quad \forall g$$

where $X$ is the input feature map, $\mathrm{Conv}_g$ denotes the group convolution, and BN is defined by:

$$\mathrm{BN}(x) = \frac{x - \mu}{\sqrt{\sigma^2 + \epsilon}}$$

with batch statistics $\mu$, $\sigma^2$, and stability constant $\epsilon$.

1. Channel Shuffle Operation: Subsequent to group convolutions and activation, channels are rearranged across groups, enhancing inter-group information exchange and effective feature utilization. The tensor is reshaped, transposed, and flattened as follows:

$$X' = \text{reshape}(X, [N, g, c/g, H, W]) \ X_{\text{shuffled}} = \text{transpose}(X', [0, 2, 1, H, W]) \ Y = \text{reshape}(X_{\text{shuffled}}, [N, c, H, W])$$

where $N$ is batch size, $c$ total channels, $g$ group count.

1. Feature Extraction and Attention via JetConv: The JetConv layer is incorporated for multi-scale feature extraction, combining asymmetric and non-asymmetric convolutions with depthwise-dilated convolutional operations, facilitating both local feature capture and receptive field expansion. Attention modules (CBAM, SAM, or ECAM architectures, as described elsewhere in JetSeg) further refine spatial or channel-wise feature emphasis.
2. Final Activation and Grouped Pointwise Convolution: A concluding activation is applied, followed by a grouped pointwise ($1 \times 1$) convolution, which serves as a feature aggregator, compressing and fusing the channel information across groups.
3. Residual Connection: The output of the final grouped pointwise convolution is added back to the block’s input, preserving both shallow and deep features and addressing the vanishing gradient issue associated with deeper networks.

2. Parameter Efficiency and Computational Optimization

JetBlock’s architecture is meticulously engineered to limit parameter growth and decrease computational complexity. By substituting conventional dense convolutions with group convolutions and pointwise operations, JetBlock reduces memory usage and inference duration—key requirements for low-power, real-time operation. The use of lightweight activation functions and channel shuffle further minimizes computational overhead, with each component tailored to embedded hardware constraints. This approach substantially decreases the model footprint and operational demands.

3. Feature Representation and Abstraction Capabilities

Despite aggressive parameter reduction measures, JetBlock maintains robust feature representation through its innovative structure:

• JetConv Layer Synergy: By combining asymmetric and depthwise-dilated convolutions, JetConv within JetBlock extracts spatially distinct and contextually rich features, essential for semantic segmentation.
• Attention Mechanisms: Attention modules direct computational focus to salient spatial or channel-specific information, mitigating representational losses typically suffered in lightweight models.
• Residual Feedback: The residual connection facilitates the entropy flow of feature representations at multiple depths of abstraction, supporting both network stability and expressivity.

This suggests that JetBlock’s design mitigates the historical trade-off between model compactness and representational fidelity in embedded segmentation architectures.

4. Quantitative Contributions to JetSeg Model Performance

The inclusion of JetBlock in JetNet is partially responsible for JetSeg’s documented performance gains. The JetSeg model achieves a reduction of 46.70M parameters and a 5.14% decrease in GFLOPs over state-of-the-art encoder-decoder segmentation models. In practice, JetSeg, via JetBlock’s efficiency, delivers up to 2x faster inference on devices such as NVIDIA Titan RTX and Jetson Xavier, substantiating the significance of block-level architectural improvements for real-time deployment on hardware-constrained platforms (Lopez-Montiel et al., 2023).

Feature	JetBlock Mechanism	Impact on JetSeg
Group Convolution	Channel partitioning	Parameter reduction
Channel Shuffle	Inter-group mixing	Enhanced representation
JetConv + Attention	Multi-scale feature	Improved accuracy
Grouped Pointwise Conv	Efficient fusion	Lowered computation
Residual Connection	Feature preservation	Stability, fidelity

5. Significance for Embedded Real-Time Semantic Segmentation

JetBlock’s design directly addresses the constraints of embedded segmentation: minimization of resource consumption and maximization of task-relevant feature abstraction. In low-power GPU-embedded scenarios, where bottlenecks arise from limited computational and memory capacity, the interplay of group convolutions, channel shuffle, and JetConv enables segmentation models to operate within strict latency and energy budgets. A plausible implication is the broader applicability of JetBlock-motivated units to other real-time visual tasks on resource-limited devices.

6. Architectural Innovations and Broader Impact

JetBlock exemplifies the architectural paradigm shift toward efficient, modular network design in semantic segmentation. Its integration of established operations—grouped convolution, attention, channel shuffle—with tailored innovations (e.g., TanhExp activation, grouped pointwise convolutions, and residual connectivity) provides a blueprint for the development of future compact, high-performing blocks in neural network architectures suitable for deployment outside datacenters. The impact extends beyond JetSeg, signaling the utility of such modular optimization for any deep visual model where hardware constraints are a primary concern.

JetBlock’s contribution lies in its ability to harmonize model compactness, inference speed, and feature richness, illustrating how architectural block engineering can meaningfully advance the capabilities of embedded real-time neural systems.