This thesis proposes "1D Convolution replacement layer ", a novel optimization for first convolution layer in CNN. This optimization enables edge friendly streaming accelerator design with a minimum drop in accuracy. This optimization reduces the number of convolution parameters in the first convolution layers of CNN, reducing the number of multiplications performed in convolution operation. In CNNs first convolution is the most memory and compute intensive as the first layer operates on input. In a streaming accelerator design, the first layer operates on streaming data, the complexity of operations and memory demand of the first layer will proportionally affect the latency of complete accelerator design. Using 1D Convolution replacement in a CNN on a N x N convolution layer after 1D replacement number of operations in each convolution window gets reduced by N times. To show the effect of 1D convolution in accelerators, streaming accelerator design for SqueezeNet is compared with 1D-SqueezeNet, SqueezeNet with 1D convolution replacement in the first layer is discussed. 1D replacement enabled edge friendly design reducing the dynamic power consumption by 7.3X, with 0.6\% drop in accuracy in SqueezeNet real-time edge accelerator.