We’ve traveled through NAS theory, search strategies, real-world applications, and efficient estimation techniques. But we’ve been operating under a hidden assumption: we optimize architectures for...

One of the biggest challenges in Neural Architecture Search is cost: evaluating thousands of candidate architectures by training them all from scratch takes months on supercomputers. In Part 3, we ...

In Part 1, we learned the foundations of Neural Architecture Search, and in Part 2, we explored search spaces and strategies. Now comes the exciting part: what have we actually discovered using NAS...

Now that we understand the foundations of neural architecture search, we can tackle the critical question: how do we actually search for good architectures? In this part, we’ll explore search space...

Neural Architecture Search (NAS) is one of the most exciting developments in deep learning—a technique that automates the design of neural network architectures rather than relying on human enginee...

Throughout this series, we’ve explored uniform quantization strategies—applying the same bit-width (8-bit, 4-bit, or even 1-bit) to all layers. But neural networks are not uniform. Some layers are ...

We’ve explored 8-bit and 4-bit quantization, achieving impressive efficiency with manageable accuracy trade-offs. But what if we push to the absolute extreme—representing weights with just a single...

Post-training quantization (Part 5) works remarkably well for INT8 deployment of large models, but it struggles when we push to 4-bit precision or work with smaller networks where representational ...

Having mastered the fundamentals of linear quantization in Parts 1-4, we now face a practical challenge: how do we actually quantize an already-trained floating-point model without retraining? Post...

We’ve built the theoretical foundation for linear quantization in Part 3, establishing the affine mapping between integers and real numbers. Now comes the payoff: transforming neural network operat...