A method for partitioning of deep convolution network for execution of computationally constrained devices at a network edge, the method comprising a processor implemented steps of: obtaining input data from a plurality of master devices, wherein the plurality of master devices are present at the network edge;estimating a runtime 'γ' by each of the plurality of master devices to run a basic unit operation of deep convolution operations, wherein the runtime 'γ' is estimated using a set of deep learning algorithm on available computationally constrained devices and the runtime 'γ' is used for estimating a speedup inference of complete convolutional operation of a given layer and wherein the runtime 'γ' used to carry out convolutional operations is dependent on one or more of a processor clock speed, memory, cache memory, bus width, load average or floating point operations per second of each device; checking a container registry and calculate if the speedup inference is achievable using the estimated runtime, and considering by the plurality of the master devices, plurality of available registered containers for partitioning of the input data to carry out convolutional operations, wherein a maximum speedup for a master-slave network is assessed by automatically adjusting the parameters comprising number of input channels and number of output channels and the number of input channels and the number of output channels fixed for input depth-wise partitioning; generating depth-wise indices for performing partitions based on an input and output depth of a convolution filter and the container registry; providing a number of the convolution filters with a predefined filter depth; partitioning the input data and the deep convolution operations by the plurality of master devices for a first network layer of the deep convolution network based on the number of convolution filters and the filter depth, wherein partitioning the deep convolution operations generates start and end indices along the depth dimension so as the partitioning by master devices can be carried out online or offline; transferring the partitioned input data and the deep convolution operations to a plurality of slave devices; executing a distributed convolution algorithm on the plurality of slave devices wherein input feature maps (IFMs) are convoluted with a set of kernel filters to generate output feature maps (OFMs) and wherein the set of kernel filters are of same depth as that of the input feature maps; merging results of the algorithm executed on the plurality of slave devices; and sending the convolutional results to a second network layer, wherein the second network layer is next to the first network layer. obtaining input data from a plurality of master devices, wherein the plurality of master devices are present at the network edge;estimating a runtime 'γ' by each of the plurality of master devices to run a basic unit operation of deep convolution operations, wherein the runtime 'γ' is estimated using a set of deep learning algorithm on available computationally constrained devices and the runtime 'γ' is used for estimating a speedup inference of complete convolutional operation of a given layer and wherein the runtime 'γ' used to carry out convolutional operations is dependent on one or more of a processor clock speed, memory, cache memory, bus width, load average or floating point operations per second of each device; checking a container registry and calculate if the speedup inference is achievable using the estimated runtime, and considering by the plurality of the master devices, plurality of available registered containers for partitioning of the input data to carry out convolutional operations, wherein a maximum speedup for a master-slave network is assessed by automatically adjusting the parameters comprising number of input channels and number of output channels and the number of input channels and the number of output channels fixed for input depth-wise partitioning; generating depth-wise indices for performing partitions based on an input and output depth of a convolution filter and the container registry; providing a number of the convolution filters with a predefined filter depth; partitioning the input data and the deep convolution operations by the plurality of master devices for a first network layer of the deep convolution network based on the number of convolution filters and the filter depth, wherein partitioning the deep convolution operations generates start and end indices along the depth dimension so as the partitioning by master devices can be carried out online or offline; transferring the partitioned input data and the deep convolution operations to a plurality of slave devices; executing a distributed convolution algorithm on the plurality of slave devices wherein input feature maps (IFMs) are convoluted with a set of kernel filters to generate output feature maps (OFMs) and wherein the set of kernel filters are of same depth as that of the input feature maps; merging results of the algorithm executed on the plurality of slave devices; and sending the convolutional results to a second network layer, wherein the second network layer is next to the first network layer.