Benchmarking AI/ML Performance Across Semiconductor Processors

Imagine that you're streaming your favorite TV show on your smartphone during your commute. Meanwhile, the AI and machine learning algorithms in your device are quietly working to enhance video quality, stabilize shaky footage, and even recommend your next show. But what happens if your phone overheats, or the battery drains too quickly? This is where AI accelerators come into play. These specialized processors are designed to deliver high computational performance without excessive power consumption, ensuring a seamless and uninterrupted experience. Additionally, AI accelerators significantly improve mobile performance by reducing latency and increasing throughput, allowing for faster response times and smoother processing of complex tasks. For hardware manufacturers, optimizing processors to efficiently manage these AI-driven workloads is no longer optional; it’s essential for staying competitive in today’s market.

The benchmarking process provides several key benefits for all stakeholders. Most importantly, it enables companies to self-assess the performance of their chipsets, identifying areas for improvement. For example, if a chipset fails to handle model weight compression or struggles with certain AI workloads, it provides valuable feedback for future hardware development. This process also offers insights into how different processors compare in terms of latency, throughput, and energy efficiency, which are essential metrics for marketing and product positioning.

It also allows companies to optimize their processors to run specific AI models faster, which is critical for applications such as image enhancement, real-time object detection etc. As AI becomes more integrated into consumer products, the ability to deliver fast and accurate results on mobile processors will be a key differentiator in the marketplace.

Key activities in AI/ML benchmarking

At the core of the project is testing over 100 AI/ML algorithms across various hardware setups, including mobile processors, GPUs, and specialized AI accelerators. These algorithms run on different devices to collect key metrics like latency and throughput.

Our team sets up the testing environment, runs the algorithms, and collects raw performance data. Optimization techniques are then applied, such as removing inefficient neural network nodes or adjusting model structures to boost performance.

Optimization techniques used

Challenges faced during benchmarking

One of the key hurdles is working with new, developer-only hardware that lacks comprehensive documentation. This requires the team to create custom scripts and methods for running the algorithms and measuring performance.

Additionally, profiling hardware such as AI accelerators—which are designed to handle AI workloads more efficiently than traditional CPUs or GPUs—has its own set of challenges. Each company has its proprietary AI accelerator, and the performance of each must be evaluated individually. This involves testing algorithms across different hardware configurations and measuring the resulting performance in terms of computation speed and power consumption.

Why are AI accelerators important?

AI accelerators are central to optimizing the performance of AI/ML algorithms on mobile devices. They deliver faster computation output while being economical in terms of power consumption. This is a major advantage in mobile and embedded systems where power efficiency is crucial.

With its benchmarking solution, Truminds assesses the effectiveness of the hardware in delivering high-performance AI capabilities while keeping power consumption under control. This is essential for improving the user experience on mobile devices, smart cameras, and IoT devices.

Conclusion

Truminds’ comprehensive AI/ML benchmarking solution applies advanced optimization techniques for profiling hardware performance and leveraging proprietary AI accelerators to help its clients stay competitive in an increasingly crowded market.