Deep dive of ACM paper - A domain specific architecture for deep neural networks

 A domain specific architecture for deep neural networks

1. for inference, weights are read-only

2. The philosophy behind the TPU mi-croarchitecture is to keep the matrixunit busy. Toward that end, the instruc-tion that reads the weights follows thedecoupled-access/execute philosophy,33in that it can complete after sendingits address but before the weight isfetched from weight memory. The ma-trix unit will stall if the input activationor weight data is not ready.

What is a neural network?

You probably have some understanding of decision trees. How do we move from a decision tree to a neural network?

A decision tree can be exactly represented as a neural network with fixed weights and step (or ReLU) activations.

From-mazes-to-minds-how-trees-keys, queries and attention shape modern AI

is CNN like how the brain uses different parts of the brain for different senses

Exactly — that’s a great intuition! 🌟 A Convolutional Neural Network (CNN) can be thought of as somewhat analogous to how the brain processes information in specialized regions. Here’s a detailed breakdown:

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1️⃣ Specialized “Regions” in CNNs

• Early layers = detect basic features (edges, corners, textures)

  • Like the primary visual cortex (V1), which detects simple patterns.

• Intermediate layers = detect more complex shapes or patterns

  • Like V2/V4 in the brain, combining simple features into more meaningful shapes.

• Deep layers = detect full objects or high-level concepts

  • Analogous to higher-level cortical areas, e.g., fusiform gyrus for faces.

Analogy: Each layer “specializes” in processing certain aspects of the input, just like different brain regions specialize in visual, auditory, or tactile input.

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2️⃣ Shared Weights → Reuse Across Space

• CNNs use shared filters (kernels) that scan across the whole image.

• This is like the brain reusing the same processing circuits across the visual field to detect edges or motion, rather than learning a separate detector for every possible location.

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3️⃣ Pooling → Summarizing Information

• CNNs often include pooling layers (max or average) to summarize local information.

• This is similar to how neurons integrate information over a region of the retina or sensory input.

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4️⃣ Multi-modal Analogy

• Just as the brain has visual, auditory, and somatosensory cortices, CNNs could be part of a larger network handling images, text, or other modalities, with each “module” specialized but connected.

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⚡ Intuitive Summary

CNN = a layered network of specialized detectors, reusing knowledge across space, and progressively building up more abstract representations — much like the brain’s sensory hierarchies.