In the previous post, we explored the difference between Training and Inference, seeing how inference-only NPUs lighten the hardware structure. One of the key keywords for this optimization was 'Reduction of Precision.'
In previous posts, we learned that MAC operations, Memory Hierarchies, and Parallel Processing (SIMD) form the foundation of deep learning hardware. Now, we stand at the biggest crossroad in the AI semiconductor market:
In previous posts, we examined the significant hardware costs incurred by the fundamental operation of artificial neurons, the MAC (Multiply-Accumulate),
In the previous post, we examined how expensive the MAC (Multiply-Accumulate) operation—the core of artificial neurons—is in terms of hardware, specifically regarding Multiplier area and Memory Bandwidth.
When starting with deep learning, the first concept we encounter is the Perceptron,
As low-power design becomes more important, Dynamic Frequency Scaling (DFS) technique,
