We propose Deterministic Binary Filters, an approach to Convolutional Neural Networks that learns weighting coefficients of predefined orthogonal binary basis instead of the conventional approach of learning directly the convolutional filters. This approach results in model architectures with significantly fewer parameters (4x to 16x) and smaller model sizes (32x due to the use of binary rather than floating point precision). We show our deterministic filter design can be integrated into well-known network architectures (such as ResNet and SqueezeNet) with as little as 2% loss of accuracy (under datasets like CIFAR-10). Under ImageNet, they result in 3x model size reduction compared to sub-megabyte binary networks while reaching comparable accuracy levels.

Binary Neural Networks (BNNs) are well-suited for deploying Deep Neural Networks (DNNs) to small embedded devices but state-of-the-art BNNs need to be trained from scratch for a long time. We show how weights from a pre-trained full-precision model can be used to speed-up training of binary networks. We show that for CIFAR-10, accuracies within 1% of the full-precision model can be achieved in just 5 epochs.

WiMAX promises an advanced framework to support Quality-of-Service (QoS) requirements of different types of applications and scheduling is a key part in its QoS provisioning. The scheduling algorithms used in this paper are based on our proposed Greedy-Latency scheduler, a modified form of Greedy algorithm which can guarantee delay requirements of real-time applications while optimising the system throughput. Our study of TCP performance in WiMAX shows that unlike UDP traffic, there are fluctuations in TCP throughput even for low traffic loads. It is seen that employing Automatic Repeat reQuest (ARQ) and setting the right TCP window size are crucial for a stable optimal TCP performance. WiMAX QoS mechanism can successfully maintain the inter-class priority between TCP traffic in Best Effort (BE) class and UDP in higher priority Real-Time Polling Service (rtPS) class. For intra-class scenarios, it is observed that TCP flows in general need a protection mechanism as the UDP traffic tend to seize the channel. The proposed Greedy-Scheduler can provide better intra-class protection for TCP flows due to its packet dropping policy.