Abstract: A method and an apparatus for processing layers in a neural network fetch Input Feature Map (IFM) tiles of an IFM tensor and kernel tiles of a kernel tensor, perform a convolutional operation on the IFM tiles and the kernel tiles by exploiting IFM sparsity and kernel sparsity, and generate a plurality of OFM tiles corresponding to the IFM tiles.

Abstract: A processor-implemented apparatus includes a forward transform module configured to transform input feature maps (IFMs) by performing a forward transform operation in a Winograd convolution (WinConv) domain, multiply and accumulate array (MAA) units configured to multiply the transformed IFMs by transformed kernels and perform a first inverse transform operation based on results of the multiplying, and an inverse transform module configured to generate output feature maps (OFMs) based on a result of the first inverse transform operation.

Abstract: A method and an apparatus for processing layers in a neural network fetch Input Feature Map (IFM) tiles of an IFM tensor and kernel tiles of a kernel tensor, perform a convolutional operation on the IFM tiles and the kernel tiles by exploiting IFM sparsity and kernel sparsity, and generate a plurality of OFM tiles corresponding to the IFM tiles.

Abstract: A method and an apparatus for processing layers in a neural network fetch Input Feature Map (IFM) tiles of an IFM tensor and kernel tiles of a kernel tensor, perform a convolutional operation on the IFM tiles and the kernel tiles by exploiting IFM sparsity and kernel sparsity, and generate a plurality of OFM tiles corresponding to the IFM tiles.

Abstract: An apparatus includes a global memory and a systolic array. The global memory is configured to store and provide an input feature map (IFM) vector stream from an IFM tensor and a kernel vector stream from a kernel tensor. The systolic array is configured to receive the IFM vector stream and the kernel vector stream from the global memory. The systolic array is on-chip together with the global memory. The systolic array includes a plurality of processing elements (PEs) each having a plurality of vector units, each of the plurality of vector units being configured to perform a dot-product operation on at least one IFM vector of the IFM vector stream and at least one kernel vector of the kernel vector stream per unit clock cycle to generate a plurality of output feature maps (OFMs).

Abstract: A method for performing a pooling operation in bitwise manner, the method includes performing a pooling operation on ternary data upon receiving an input ternary vector, receiving an input binary vector, providing a fused hardware for performing the pooling operation on any of the received binary and the ternary data, and executing the pooling operation performed bitwise through the fused hardware.

Abstract: Disclosed is a hybrid traversal apparatus and method for a convolution neural network (CNN) accelerator architecture that receives input feature map (IFM) microbatches from a pixel memory and receiving kernel microbatches from a kernel memory, multiplies the IFM microbatches by the kernel microbatches while reusing the kernel microbatches based on a kernel reuse factor for at least one of a direct convolution (DConv) or a Winograd convolution (WgConv), to obtain output feature map (OFM) microbatches, and writes the generated OFM microbatches to the pixel memory, after quantization, non-linear function, and pooling on a result of the multiplying.

Abstract: A processor-implemented method for generating Output Feature Map (OFM) channels using a Convolutional Neural Network (CNN), include a plurality of kernels, includes generating at least one encoded Similar or Identical Inter-Kernel Weight (S/I-IKW) stream, converting, similar and identical weights in the at least one non-pivot kernel to zero to introduce sparsity into the at least one non-pivot kernel, broadcasting at least one value to the at least one non-pivot kernel, and generating at least one OFM channel by accumulating an at least one previous OFM value with any one or any combination of any two or more of a convolution of non-zero weights of the pivot kernel and pixels of the Input Feature Map (IFM), the at least one broadcasted value, and a convolution of non-zero weights of the at least one non-pivot kernel and pixels of the IFM.

Abstract: A method and an apparatus for processing layers in a neural network fetch Input Feature Map (IFM) tiles of an IFM tensor and kernel tiles of a kernel tensor, perform a convolutional operation on the IFM tiles and the kernel tiles by exploiting IFM sparsity and kernel sparsity, and generate a plurality of OFM tiles corresponding to the IFM tiles.