Jetson Nano-S: Compact Edge AI Platform

• Jetson Nano-S is a compact, low-power edge AI platform featuring an NVIDIA Maxwell GPU and quad-core ARM CPU for efficient deep learning and signal processing in constrained environments.
• Optimized with techniques such as FP16/INT8 quantization and TensorRT, it achieves up to a 7× speedup in inference while reducing energy consumption.
• Designed for diverse applications from CubeSats to brain-computer interfaces, it supports robust signal processing, model compression, and real-time AI deployments.

Jetson Nano-S is a compact, low-power edge AI computing platform that integrates an NVIDIA GPU to enable real-time deep learning inference and signal processing in resource-constrained environments. Occupying a small form factor suited for embedded and autonomous systems—including space-constrained deployments such as CubeSats—the Jetson Nano-S builds on hardware-efficient execution and supports a wide range of deep neural network architectures and application domains.

1. Hardware Architecture and Edge Deployment Context

Jetson Nano-S features a Maxwell-based NVIDIA GPU (128 CUDA cores, 921 MHz), paired with a quad-core ARM CPU and 4 GB LPDDR4 shared memory. Its thermal design power typically peaks at 10 W, emphasizing operation under strict energy budgets. The board supports multiple storage configurations, primarily Micro SD for the operating system and USB HDD for data storage (K. et al., 24 Sep 2025), with the possibility of additional interfaces on variant Nano models.

The system is engineered for deployment at the edge, including real-time computer vision, robotics, industrial automation, and scientific workloads in harsh environments such as onboard CubeSats (Lofqvist et al., 2020). Key software includes Linux for Tegra (L4T), CUDA libraries, and embedded deep learning frameworks (TensorRT, PyTorch, ONNX).

2. Deep Learning Model Optimization and Inference

Model deployment on Jetson Nano-S leverages hardware-aware optimizations. Conversion of models from PyTorch to ONNX formats and subsequent refinement with TensorRT enables weight precision calibration (e.g., FP16/INT8 quantization), kernel fusion, and operator auto-tuning (Swaminathan et al., 25 Jun 2024). These optimizations, together with careful architectural choices—such as lightweight backbones (MobileNetV2, EfficientNet)—yield real-time inference under tight power and memory constraints.

On Jetson Nano-S, optimized models (including AlexNet, VGG, ResNet, SqueezeNet, DenseNet, ShuffleNet-V2, MobileNet-V2, and custom 3D-CNNs) show an average speedup of ~7× over non-optimized implementations. Hardware-specific tuning not only reduces latency but also substantially lowers overall energy consumption and carbon footprint.

The board achieves competitive inference performance: for example, MobileNetV2 can complete 5,000 image inferences in ~685 s (0.137 s/image) in CPU mode, and 0.020–0.023 s/image with GPU acceleration (Baller et al., 2021).

3. Compression, Quantization, and Model Design for Edge

Jetson Nano-S supports deployment of highly compressed and quantized models. Squeezed Edge YOLO, for instance, employs aggressive convolution weight reduction—replacing 3×3 convolutions with 1×1 and quantizing weights to 8-bit integers—resulting in a model that is ~8× smaller (~7.5 MB), improving throughput by 3.3× and energy efficiency by 76% compared to earlier edge YOLO versions (Humes et al., 2023).

Similarly, real-time monocular depth estimation is achieved using custom encoders—RT-MonoDepth and RT-MonoDepth-S—which avoid heavy normalization and use efficient upsampling, yielding frame rates of 18.4–30.5 FPS for 640×192 RGB images and maintaining high accuracy (Feng et al., 2023).

4. Signal Processing and Brain-Computer Interface Integration

Jetson Nano-S is adapted to real-time biosignal processing, acting as both acquisition and processing unit for EEG, EMG, and ECG signals. The JNEEG shield, equipped with Texas Instruments ADS1299 ADC and dry Ag/AgCl electrodes, provides 8-channel signal input arranged per the International 10–20 system (Rakhmatulin, 14 May 2024).

Onboard bandpass filtering, artifact rejection, and deep feature extraction via CNNs or wavelet transforms are executed directly on the Nano-S, enabling closed-loop brain-computer interface (BCI) operations. Tests confirm low internal noise (~1 µV) and high fidelity in alpha wave detection, chewing/blinking artifact discrimination, and EEG classification (Rakhmatulin, 2023).

5. Application-Specific Implementations

Jetson Nano-S is deployed across diverse real-world domains:

• Space autonomy: Deep CNN object detectors (SSD, R-FCN) run on Nano-S with optimized image compression and scaling, making real-time aerial detection feasible within CubeSat memory and energy limits. Combined lossless compression and scaling yield up to 100% runnable dataset, 1050 MB average RAM savings, and near-constant accuracy (Lofqvist et al., 2020).
• Face and waste detection: Embedded SSD or MobileNet models, trained using domain-specific datasets and inference-optimized with TensorRT, provide >95% classification accuracy in facial detection and automated recycling bins (Rehman et al., 2021, Li et al., 2022). Rapid throughput (e.g., up to 40 IPS at 4.7 W) and multitasking (UI integration) are supported.
• Gesture recognition: Spiking recurrent neural networks with learnable liquid time constants yield a 14× improvement in power efficiency over desktop GPUs; the Nano-S sustains recognition at 155 FPS and 1.5 W (Varposhti et al., 23 Aug 2024).
• Clinical summarization: Dual-stage architectures partition retrieval and summarization tasks across Nano-R (Retrieve) and Nano-S (Summarize), hosting quantized small LLMs (SLMs) for privacy-preserving, offline summarization of EHRs with sub-30 s latency. LLM-as-Judge frameworks evaluate outputs for factual accuracy and completeness, employing weighted scoring formulas (Wu et al., 5 Oct 2025).

6. Limitations, Security Implications, and Benchmarking

Nano-S, while robust, is limited by RAM (4GB on classic models), shared CPU-GPU memory, and modest storage interface (primarily Micro SD and USB HDD). Large models may force swapping, affecting inference or training times (Baller et al., 2021, K. et al., 24 Sep 2025).

Security risks include vulnerability to electromagnetic side-channel attacks—architectural details of deployed CNNs can be extracted from EM traces with near-perfect accuracy using deep learning classifiers; custom network designs or EM shielding are required as countermeasures (Horvath et al., 24 Jan 2024).

Comparative benchmarks consistently show Jetson Nano-S outpaces CPU-only and low-end accelerators (Raspberry Pi/Tinker Edge) in inference speed, energy per request (e.g., 0.09–0.22 mWh for state-of-the-art detectors), and real-time viability, despite higher idle power consumption (Alqahtani et al., 25 Sep 2024). For DNN training, Nano-S is best suited for lightweight models, with predictably reproducible epoch timings and energy usage aided by pipelined DataLoader configurations and strategic memory caching (K. et al., 24 Sep 2025).

7. Outlook: Federated Learning and Edge AI Scalability

Emerging studies propose extending Jetson Nano-S capabilities to federated learning, autonomous edge decision making, and complex AI workloads. The documented resource inter-dependencies (I/O, CPU, GPU, disk caching, power mode scaling) and predictive linear models for epoch time/energy (e.g.,

$T_i = a x_1 + b x_2 + c x_3 + d x_4 + e$

for CPU frequency, core count, GPU frequency, memory frequency) (K. et al., 24 Sep 2025) facilitate task scheduling, energy optimization, and scalable real-time deployment across heterogeneous edge fleets.

These empirical characterizations provide actionable guidance for practitioners seeking to optimize deep learning models for speed, energy, and accuracy on Jetson Nano-S, and offer methodological precedents for sustainable, privacy-respecting, and robust edge AI implementations.