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The optimization of Machine Learning models has always been one of EVS’s core pillars. Today, with increasingly complex architectures, finding the best balance between accuracy, latency, and memory footprint is essential. These dimensions are orthogonal, and optimizing them all at once is challenging: improving one can come at the expense of another. True expertise lies in maximizing results by identifying the ideal trade-off for each application scenario.

Origins: From Small Models to Neural Networks

When we began working with Artificial Intelligence, “classical” machine learning models (e.g., Support Vector Machines) were fairly limited in size, yet we already paid close attention to efficiency. Why? Because we were operating on embedded systems at a time when affordable, high-performance hardware simply did not exist. This led us to design ad hoc hardware accelerators or refactor software algorithms to make the most of the resources available. Often, the winning strategy was to combine both approaches. Switching to deep neural networks then opened up levels of accuracy once unimaginable using traditional methods, but also introduced new costs in terms of computation time, memory, energy, and overall computational resources.

The Four “Costs” of Running a Model

  1. Time – Larger, more accurate models need more time to process data. In real-time or safety-critical applications a few extra milliseconds can make all the difference.
  2. Memory – Advanced neural networks require a great deal of RAM and storage for their parameters. On embedded or edge devices, memory is limited.
  3. Energy – Higher computational power entails greater electricity consumption. In contexts like automotive—or battery-powered IoT devices—an overly power-hungry model can be impractical.
  4. Hardware Cost – High-end (flagship) GPUs or HPC servers are expensive and draw significant power, raising the final product’s price.

Why Is Optimization Essential?

We have observed that a “large” neural network often delivers excellent results, but to function in real-world scenarios as we mentioned before (cars, mobile devices, industrial systems), one must minimize resource usage without excessively degrading the quality of performance. This is where optimization comes into play: reducing the model’s complexity—in other words, lowering its “costs” while affecting accuracy and outputs as little as possible.

How Does Optimization Work?

  • Parameter Pruning: Eliminating those weights in the network that contribute little to the final output (just as the human brain prunes superfluous connections while growing).
  • Quantization: Moving from an FP32 representation to formats with lower precision (e.g., INT8 or even binary), thus reducing the complexity of individual calculations and the memory needed to store parameters.
  • Optimized Modules: Using blocks with a higher “abstraction-to-computation” ratio (e.g., depthwise separable layers instead of heavier single convolutional layers) to better exploit the target hardware’s capabilities.
  • Optimized Operations: For example, switching to the Winograd domain can exponentially boost convolution efficiency.

EVS's Approach: From “Small” to “Large” Efficiently

  1. Historical Experience: We started with “shallow” models (SVMs and small fully-connected networks), learning to “squeeze” every clock cycle to maximize efficiency on custom hardware.
  2. Continuous Evolution: Even as we transitioned first to convolutional networks and then to transformers, we maintained a strong focus on efficiency, exploring all available optimization techniques.
  3. Design for Dedicated Hardware: We collaborate with partners to create solutions optimized for their specific hardware. These solutions must operate in environments where time, memory, and energy constraints are critical

Why Is This a Competitive Factor?

  • Cost Reduction: An optimized model can run on more affordable hardware, thereby lowering the final product’s price.
  • Scalability: Harnessing devices with limited power paves the way for “frontier” contexts (edge computing) where AI can truly make a difference.
  • Faster Response: Less computational load means shorter inference times, a key factor in real-time systems.
  • Lower Energy Consumption: Optimization decreases environmental impact, extends battery life, and reduces energy costs.

Conclusion

Optimization is an enabler for bringing Artificial Intelligence to every sector—from biomedical to automotive, from Industry 4.0 to transportation. At EVS, we have been focusing on these techniques for years and will continue to do so, because only lightweight, efficient, and flexible models can provide truly scalable and sustainable AI solutions.