In the previous 3 Mappings of Conv Operations, we looked at a strategy to sacrifice memory and gain computational speed (GEMM) through the Im2Col method when processing standard convolutions in hardware.
In the previous 3 Mappings of Conv Operations, we explored the massive trade-off (like Im2Col) of exchanging memory ...
In the previous post, we learned that hardware loves CNNs (Convolutional Neural Networks) because of Locality and Data Reuse. Theoretically, CNNs seem like the perfect hardware-friendly algorithm.
In the previous post, we confirmed how inefficient MLP (Fully Connected Layer) is from a hardware perspective. Due to its structure of fetching a weight once,
In previous posts, we learned about Quantization techniques to shave down data size to reduce hardware costs. So, why do we try so desperately to reduce data size?
In the previous post, we examined how differences in Number Formats affect hardware area and power consumption. We established that FP32 ...
