Extended Bit-Plane Compression for Convolutional Neural Network Accelerators

After the tremendous success of convolutional neural networks in image classification, object detection, speech recognition, etc., there is now rising demand for deployment of these compute-intensive ML models on tightly power constrained embedded and mobile systems at low cost as well as for pushing the throughput in data centers. This has triggered a wave of research towards specialized hardware accelerators. Their performance is often constrained by I/O bandwidth and the energy consumption is dominated by I/O transfers to off-chip memory. We introduce and evaluate a novel, hardware-friendly compression scheme for the feature maps present within convolutional neural networks. We show that an average compression ratio of 4.4x relative to uncompressed data and a gain of 60% over existing method can be achieved for ResNet-34 with a compression block requiring <300 bit of sequential cells and minimal combinational logic

Computational Music Biofeedback for Stress Relief

The purpose of our project is to use EEG technology to combat stress in our daily lives. One of the most accessible EEG technologies that targets this challenge is the Muse headband, a wearable device that pairs with a phone application to help users train their brains to relax. The applications main goal is to help users train their brain to be more relaxed by monitoring and reporting their levels of stress. However, one of the shortcomings we noticed is that the constant notifications of how stressed we are actually adds to the level of stress as opposed to helping train our brains towards a more relaxed state. In order to improve this solution, our program uses the live brain waves transmitted by the Muse headband and feedforward techniques to not only track brain users activity, but also help the user move towards a more relaxed state using music and binaural beats. While we werent able to test the system on an unbiased population due to time constraints, preliminary exploration on ourselves on both short term and longer term sessions shows that longer uses of our system led to more a relaxed state

Towards Accurate and High-Speed Spiking Neuromorphic Systems with Data Quantization-Aware Deep Networks

Deep Neural Networks (DNNs) have gained immense success in cognitive applications and greatly pushed today's artificial intelligence forward. The biggest challenge in executing DNNs is their extremely data-extensive computations. The computing efficiency in speed and energy is constrained when traditional computing platforms are employed in such computational hungry executions. Spiking neuromorphic computing (SNC) has been widely investigated in deep networks implementation own to their high efficiency in computation and communication. However, weights and signals of DNNs are required to be quantized when deploying the DNNs on the SNC, which results in unacceptable accuracy loss. %However, the system accuracy is limited by quantizing data directly in deep networks deployment. Previous works mainly focus on weights discretize while inter-layer signals are mainly neglected. In this work, we propose to represent DNNs with fixed integer inter-layer signals and fixed-point weights while holding good accuracy. We implement the proposed DNNs on the memristor-based SNC system as a deployment example. With 4-bit data representation, our results show that the accuracy loss can be controlled within 0.02% (2.3%) on MNIST (CIFAR-10). Compared with the 8-bit dynamic fixed-point DNNs, our system can achieve more than 9.8x speedup, 89.1% energy saving, and 30% area saving.Comment: 6 pages, 4 figure