The most important requirement to make any near-eye display successful is to provide a comfortable visual experience. This requirement has many boxes to check: having high resolution and wide field of view, being lightweight, having small form factor, and supporting focus cue. Like 3D TVs and movies, near-eye displays also need to solve the vergence and accommodation conflicts. In current Virtual Reality (VR) displays, the user fixates his focus on the fixed focal plane, and the disparity in the pre-processed content drives the eye to verge and creates a 3D sensation.

Over the last few years, virtual reality has re-emerged as a technology that is now feasible at low cost via inexpensive cellphone components. In particular, advances of high-resolution micro displays, low-latency orientation trackers, and modern GPUs facilitate extremely immersive experiences. To facilitate comfortable long-term experiences and wide-spread user acceptance, however, the vergence-accommodation conflict inherent to all stereoscopic displays will have to be solved.

In this paper we focus on the linear algebra theory behind feedforward (FNN) and recurrent (RNN) neural networks. We review backward propagation, including backward propagation through time (BPTT). Also, we obtain a new exact expression for Hessian, which represents second order effects. We show that for t time steps the weight gradient can be expressed as a rank-t matrix, while the weight Hessian is as a sum of t2 Kronecker products of rank-1 and WTAW matrices, for some matrix A and weight matrix W.

Holograms have high resolution and great depth of field allowing the eye to view a scene much like seeing through a virtual window. Unfortunately, computer generated holography (CGH) does not deliver the same promise due to hardware limitations under plane wave illumination and large computational cost. Light field displays have been popular due to their capability to provide continuous focus cue. However, light field displays suffer from the trade offs between spatial and angular resolution, and do not model diffraction.

A variety of applications such as virtual reality and immersive cinema require high image quality, low rendering latency, and consistent depth cues. 4D light field displays support focus accommodation, but are more costly to render than 2D images, resulting in higher latency. The human visual system can resolve higher spatial frequencies in the fovea than in the periphery. This property has been harnessed by recent 2D foveated rendering methods to reduce computation cost while maintaining perceptual quality.

Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. High performance and extreme energy efficiency are critical for deployments of CNNs, especially in mobile platforms such as autonomous vehicles, cameras, and electronic personal assistants. This paper introduces the Sparse CNN (SCNN) accelerator architecture, which improves performance and energy efficiency by exploiting the zero-valued weights that stem from network pruning during training and zero-valued activations that arise from the common ReLU operator.