Abstract: Apparatuses, systems, and techniques to control operation of a memory cache. In at least one embodiment, cache guidance is specified within application source code by associating guidance with declaration of a memory block, and then applying specified guidance to source code statements that access said memory block.

Abstract: Methods and apparatus relating to techniques for incremental network quantization. In an example, an apparatus comprises logic, at least partially comprising hardware logic to determine a plurality of weights for a layer of a convolutional neural network (CNN) comprising a plurality of kernels; organize the plurality of weights into a plurality of clusters for the plurality of kernels; and apply a K-means compression algorithm to each of the plurality of clusters. Other embodiments are also disclosed and claimed.

Abstract: Methods and apparatus relating to techniques for incremental network quantization. In an example, an apparatus comprises logic, at least partially comprising hardware logic to determine a plurality of weights for a layer of a convolutional neural network (CNN) comprising a plurality of kernels; organize the plurality of weights into a plurality of clusters for the plurality of kernels; and apply a K-means compression algorithm to each of the plurality of clusters. Other embodiments are also disclosed and claimed.

Abstract: Embodiments described herein provide a processing apparatus comprising compute logic to generate output feature map data for a convolutional neural network (CNN) and write the feature map data to a memory buffer; a direct memory access (DMA) controller including a feature map encoder, the DMA controller to read the feature map data from the memory buffer, encode the feature map data using one of multiple encode algorithms, and write encoded feature map data to memory coupled with the processing apparatus; and wherein the compute logic is to read the encoded feature map data from the memory in an encoded format and decode the encoded feature map data while reading the encoded feature map data.

Abstract: Methods and apparatus relating to techniques for incremental network quantization. In an example, an apparatus comprises logic, at least partially comprising hardware logic to determine a plurality of weights for a layer of a convolutional neural network (CNN) comprising a plurality of kernels; organize the plurality of weights into a plurality of clusters for the plurality of kernels; and apply a K-means compression algorithm to each of the plurality of clusters. Other embodiments are also disclosed and claimed.

Abstract: A processor, including: decode circuitry to decode instructions; a data cache unit including circuitry to cache data for the processor; and an approximate matrix multiplication (AMM) circuit including: a data receptor circuit to receive a weight vector w and an input vector x, both of size N, and a compression regulating parameter n; a factorizer circuit to factorize w into w?B·s, by computing a binary factorized matrix B of size N×n, and a dictionary vector s of size n; and a binary multiplier circuit to compute w^T x?(B·s)^T x=s^T(B^T x), the binary multiplier circuit comprising a hardware accelerator circuit to compute an array product B^T x).

Abstract: A processor, including: decode circuitry to decode instructions; a data cache unit including circuitry to cache data for the processor; and an approximate matrix multiplication (AMM) circuit including: a data receptor circuit to receive a weight vector w and an input vector x, both of size N, and a compression regulating parameter n; a factorizer circuit to factorize w into w?B·s, by computing a binary factorized matrix B of size N×n, and a dictionary vector s of size n; and a binary multiplier circuit to compute w?T x?(B·s)?T x=s?T (B)?T x), the binary multiplier circuit comprising a hardware accelerator circuit to compute an array product B?T x).

Abstract: Embodiments described herein provide a processing apparatus comprising compute logic to generate output feature map data for a convolutional neural network (CNN) and write the feature map data to a memory buffer; a direct memory access (DMA) controller including a feature map encoder, the DMA controller to read the feature map data from the memory buffer, encode the feature map data using one of multiple encode algorithms, and write encoded feature map data to memory coupled with the processing apparatus; and wherein the compute logic is to read the encoded feature map data from the memory in an encoded format and decode the encoded feature map data while reading the encoded feature map data.