Texas Instruments Corporation

Kumar Desappan

Pramod Kumar Swami

Soyeb Noormohammed Nagori

Manu Mathew

Mihir Narendra Mody

Deepak Kumar Poddar

Hrushikesh Tukaram Garud

Kedar Satish Chitnis

Shyam Jagannathan

Yashwant Dutt

Piyali Goswami

Debapriya Maji

David Smith

Techniques for executing machine learning (ML) models including receiving an indication to run an ML model on a processing core; receiving a static memory allocation for running the ML model on the processing core; determining that a layer of the ML model uses more memory than the static memory allocated; transmitting, to a shared memory, a memory request for blocks of the shared memory; receiving an allocation of the requested blocks; running the layer of the ML model using the static memory and the range of memory addresses; and outputting results of running the layer of the ML model.

Memory sharing for machine learning processing

In described examples of a method for quantizing data for a convolutional neural network (CNN) is provided. A set of data is received and quantized the using a power-of-2 parametric activation (PACT2) function. The PACT2 function arranges the set of data as a histogram and discards a portion of the data corresponding to a tail of the histogram to form a remaining set of data. A clipping value is determined by expanding the remaining set of data to a nearest power of two value. The set of data is then quantized using the clipping value. With PACT2, a model can be quantized either using post training quantization or using quantization aware training. PACT2 helps a quantized model to achieve close accuracy compared to the corresponding floating-point model.

Parametric power-of-2 clipping activations for quantization for convolutional neural networks

Several methods and systems for masking multimedia data are disclosed. In an embodiment, a method for masking includes performing a prediction for at least one multimedia data block based on a prediction mode of a plurality of prediction modes. The at least one multimedia data block is associated with a region of interest (ROI). A residual multimedia data associated with the at least one multimedia data block is generated based on the prediction. A quantization of the residual multimedia data is performed based on a quantization parameter (QP) value. The QP value is variable such that varying the QP value controls a degree of masking of the ROI.

Methods and systems for masking multimedia data

A method for convolution in a convolutional neural network (CNN) is provided that includes accessing a coefficient value of a filter corresponding to an input feature map of a convolution layer of the CNN, and performing a block multiply accumulation operation on a block of data elements of the input feature map, the block of data elements corresponding to the coefficient value, wherein, for each data element of the block of data elements, a value of the data element is multiplied by the coefficient value and a result of the multiply is added to a corresponding data element in a corresponding output block of data elements comprised in an output feature map.

Reduced complexity convolution for convolutional neural networks

An example image feature extraction system comprises an encoder neural network having a first set of layers and a decoder neural network having a second set of layers and a third set of layers. The encoder neural network receives an input image, processes the input image through the first set of layers, and computes an encoded feature map based on the input image. The decoder neural network receives the encoded feature map, processes the encoded feature map through the second set of layers to compute a keypoint score map, and processes the encoded feature map through at least a portion of the third set of layers to compute a feature description map.

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Bengaluru, India

Kumar Desappan