In the smart wearables use case, language models are embedded into devices such as smartwatches, fitness trackers, or health monitoring systems to provide insights, and instructions, or interact with users through natural language processing. Small language models (SLMs) often offer key advantages in terms of efficiency and speed compared to large language models (LLMs) due to hardware constraints in wearables.

Use Case: Real-time Health Monitoring and Interaction in Smart Wearables

Scenario

A fitness tracker monitors a user’s heart rate, sleep patterns, and daily activities while providing health suggestions through voice or text. The device uses AI models to analyze health data in real-time and interact with the user when necessary. Both SLMs and LLMs are evaluated for their efficiency in delivering real-time health insights and recommendations.

Key Metrics for Comparison

• Latency: Time to process health data and generate responses.

• Memory Usage: RAM is required to run the model on a wearable device.

• Power Consumption: Battery impact of running the model continuously.

• Response Accuracy: Correctness and relevancy of health insights or prompts.

• Hardware Requirements: CPU power and capability needed to run the models.

Metric

• Model Size

• Latency (average)

• Memory Usage (RAM)

• Battery Consumption

• Response Accuracy

• Hardware Requirements

Small Language Model (SLM)

• 50M parameters

• 10 ms/query

• 100 MB

• Low (2% per day)

• 80%

• Basic low-power CPU

Large Language Model (LLM)

• 1.2B parameters

• 1,000 ms/query

• 6 GB

• High (15% per day)

• 90%

• High-end chip, possibly offloaded to the cloud

Technical Insights

1. Latency and Processing: For smart wearables, speed and real-time responses are critical. The SLM processes data with just 10 ms latency, allowing it to quickly interpret sensor data (e.g., heart rate spikes, abnormal sleep patterns) and give immediate feedback, such as suggesting a break if heart rate is too high. The LLM, however, takes 1,000 ms (1 second) for the same task, introducing noticeable delays in real-time feedback. For wearables, this delay could reduce user engagement and effectiveness.

2. Memory and Power Constraints: The SLM, requiring only 100 MB of RAM, easily fits into the limited memory resources of a typical wearable device. In contrast, the LLM needs around 6 GB of memory, making it impractical to run natively on most wearables, often requiring cloud offloading, which introduces privacy concerns and additional latency. Battery consumption is also significantly lower with the SLM, impacting only 2% of the battery per day, while the LLM drains 15%, which would significantly reduce the life of a wearable that is typically expected to last days without recharging.

3. Accuracy Trade-offs: While the LLM has a higher accuracy (90%) in understanding complex health queries, the SLM achieves a satisfactory 80% accuracy, which is typically sufficient for basic tasks like detecting activity levels, interpreting heart rate data, or responding to user queries like "How many steps today?" The slight trade-off in accuracy is balanced by the improved speed and efficiency that ensures a smooth user experience.

4. Hardware Requirements: Smart wearables are designed with low-power CPUs, and the SLM's efficient memory usage and computational needs align well with the processing capabilities of these devices. The LLM, however, would require a more powerful CPU (or even GPU), pushing the boundaries of wearable device design and leading to higher costs and bulkier devices.

Business Insights

1. Real-time User Interaction: In a smart wearable, the SLM’s ability to generate responses in 10 ms makes it ideal for seamless real-time feedback. For example, when a user’s heart rate spikes during exercise, the SLM can quickly alert them to slow down. The LLM, though more accurate, is far slower, making it less practical for immediate health or fitness feedback where real-time alerts are crucial for user safety and engagement.

2. Battery Life: Wearable users expect long battery life, often requiring a device to last several days on a single charge. The SLM, with its low power consumption (only 2% per day), ensures the wearable remains functional for extended periods, which is critical for consumer satisfaction. In contrast, LLMs consume much more energy, significantly reducing battery life, making them unsuitable for devices where battery longevity is a key selling point.

3. Cost and Scalability: SLMs can be implemented on affordable hardware, making them a cost-effective solution for companies looking to produce mass-market wearables. The hardware requirements of an LLM are more expensive, both in terms of production and power consumption, driving up the costs of manufacturing. For businesses targeting broad consumer markets, choosing SLMs allows for greater profit margins and competitive pricing.

4. User Experience and Privacy: Since SLMs can operate fully on-device, all data processing happens locally, providing enhanced user privacy and eliminating the need to upload sensitive health data to the cloud. This reduces concerns about data security and gives users more confidence in using the product. LLMs, due to their size, typically require cloud-based processing, raising privacy concerns, particularly with health-related data, which could affect the adoption of the wearable.

Benchmarking Example

Consider a smart wearable that tracks heart rate and activity level, generating 100 data points per hour and requiring real-time interaction for the user’s daily workout session.

• SLM Processing Time: 10 ms/query → 0.001 seconds per data point.

• LLM Processing Time: 1,000 ms/query → 1 second per data point.

For a one-hour workout session generating 100 data points, the SLM processes all data points in 1 second, while the LLM takes 100 seconds (over 1.5 minutes). The faster processing time allows the SLM to provide immediate feedback, enhancing user engagement and ensuring real-time health monitoring.

Conclusion

In the smart wearables domain, such as fitness trackers and health monitoring devices, small language models (SLMs) deliver superior performance in terms of speed, efficiency, and cost-effectiveness compared to large language models (LLMs).

• SLMs are better suited for real-time applications, providing near-instantaneous responses (10 ms), making them ideal for real-time health monitoring, which is a critical function in wearables.

• Long battery life and on-device processing ensure that SLM-powered devices are more user-friendly, privacy-focused, and cost-effective, compared to LLM-powered devices which drain battery life faster and may require cloud resources, raising privacy concerns.

• For businesses, choosing SLMs allows for the creation of affordable, scalable, and efficient wearables that meet consumer expectations without compromising on essential features such as real-time interaction and privacy.

While LLMs may offer marginally higher accuracy, the efficiency of SLMs far outweighs this benefit in the context of smart wearable devices where speed, battery efficiency, and cost are paramount.