Abstract: Example systems, methods, and apparatus to generate optimized models for Internet of Things device are disclosed. An example apparatus includes a data receiver to collect data from a sensor of an internet of things device based a first sampling frequency and a buffer having a first buffer size; a model trainer to train a model based on the data collected from the sensor; a buffer analyzer to select a second sampling frequency and to reduce the buffer to a second buffer size, the model trainer to update the model based on the second buffer size; and a platform analyzer to: determine a duration of time that that internet of things device will take to analyze sensor data based on the updated model.

Abstract: Example systems, methods, and apparatus to generate optimized models for Internet of Things device are disclosed. An example apparatus includes a data receiver to collect data from a sensor of an internet of things device based a first sampling frequency and a buffer having a first buffer size; a model trainer to train a model based on the data collected from the sensor; a buffer analyzer to select a second sampling frequency and to reduce the buffer to a second buffer size, the model trainer to update the model based on the second buffer size; and a platform analyzer to: determine a duration of time that that internet of things device will take to analyze sensor data based on the updated model.

Abstract: An apparatus is provided to compress DNNs using filter pruning on a per-group basis. For example, the apparatus accesses a trained DNN that includes a plurality of layers. The apparatus generates a sequential graph representation of the plurality of layers. The sequential graph representation includes a sequence of nodes. Each node is a graph representation of a layer. The apparatus clusters the layers into layer groups. A layer group includes one or more layers. The apparatus determines a pruning ratio for a layer group and prunes the filters of the layers in the layer group based on the pruning ratio. The apparatus may cluster the layers and determine the pruning ratio by using a GNN. The apparatus generates compressed layers from the layers in the layer group through the filter pruning process. The apparatus further updates the DNN by replacing the layers in the layer group with the compressed layers.

Abstract: An apparatus is provided to use NEMO search to train GNNs that can be used for mixed-precision quantization of DNNs. For example, the apparatus generates a plurality of GNNs. The apparatus further generates a plurality of new GNNs based on the plurality of GNNs. The apparatus also generates a sequential graph for a first DNN. The first DNN includes a sequence of quantizable operations, each of which includes quantizable parameters and is represented by a different node in the sequential graph. The apparatus inputs the sequential graph into the GNNs and new GNNs and evaluates outputs of the GNNs and new GNNs based on conflicting objectives of reducing precisions of the quantizable parameters of the first DNN. The apparatus then selects a GNN from the GNNs and new GNNs based on the evaluation. The GNN is to be used for reducing precisions of quantizable parameters of a second DNN.

Abstract: Example systems, methods, and apparatus to generate optimized models for Internet of Things device are disclosed. An example apparatus includes a data receiver to collect data from a sensor of an internet of things device based a first sampling frequency and a buffer having a first buffer size; a model trainer to train a model based on the data collected from the sensor; a buffer analyzer to select a second sampling frequency and to reduce the buffer to a second buffer size, the model trainer to update the model based on the second buffer size; and a platform analyzer to: determine a duration of time that that internet of things device will take to analyze sensor data based on the updated model.