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,
When designing RTL, there are many times when you need modules that perform similar functions but differ only in bit width or pipeline depth.
