.jpg)
In the world of deep learning, neural networks often harbor significant redundancy in their hidden states. However, most compression techniques focus on weights, neurons, or quantized representations, without analyzing the dynamic role of those internal states. A novel approach proposes using concepts of controllability and observability —taken from systems theory— to empirically and soundly reduce the order of hidden layers. Instead of blindly pruning or quantizing, reachability and observability Gramians are constructed from neuron outputs and network Jacobians, thus obtaining ranks that indicate which dimensions are truly necessary. This allows redesigning more compact architectures with minimal loss of accuracy, something crucial for implementing AI for businesses in resource-constrained environments.
The central idea is to treat a trained neural network as a depth-indexed nonlinear dynamic system. Using data from intermediate activations and output sensitivities, matrices are computed that measure the ability to reach relevant states and to distinguish them from the output. Dimensions with low contribution are removed, generating a 'balanced realization' that retains only essential information. This method has demonstrated parameter reductions of over 70% in experiments with datasets such as MNIST and CIFAR-10, maintaining accuracy almost intact and speeding up inference up to 3 times. For a company developing custom applications, having lightweight and efficient models is a competitive advantage, especially when integrated into edge devices or in cloud services aws and azure pipelines where computational cost matters.
From a business perspective, this technique opens the door to deploying artificial intelligence in environments with memory or latency constraints, without sacrificing performance. At Q2BSTUDIO, as a software and technology development company, we apply these principles to optimize AI models that we then integrate into complete solutions: from AI agents that automate processes to cybersecurity systems that detect anomalies in real time. Compression based on controllability-observability fits perfectly with our philosophy of offering business intelligence services that make the most of data, using tools like Power BI to visualize the impact of reductions. Ultimately, it is about building custom software that not only works, but does so efficiently and sustainably, adapting to the specific needs of each client.
