CUTLASS 2.4.0

CUTLASS 2.4

• Implicit GEMM convolution kernels supporting CUDA and Tensor Cores on NVIDIA GPUs
  • Operators: forward (Fprop), backward data gradient (Dgrad), and backward weight gradient (Wgrad) convolution
  • Data type: FP32, complex, Tensor Float 32 (TF32), BFloat16 (BF16), Float16, Int4, Int8, Int32
  • Spatial dimensions: 1-D, 2-D, and 3-D
  • Layout: NHWC, NCxHWx
• Implicit GEMM convolution components:
  • Global memory iterators supporting Fprop, Dgrad, and Wgrad
  • MmaMultistage for implicit GEMM convolution for NVIDIA Ampere architecture
  • MmaPipeline for implicit GEMM convolution for NVIDIA Volta and Turing architectures
  • Documentation describing Implicit GEMM Convolution algorithm and implementation