Monte Carlo ray tracing offers the capability of rendering scenes with large numbers of area light sources---lights can be sampled stochastically and shadowing can be accounted for by tracing rays, rather than using shadow maps or other rasterization-based techniques that do not scale to many lights or work well with area lights. Current GPUs only afford the capability of tracing a few rays per pixel at real-time frame rates, making it necessary to focus sampling on important light sources.

We propose a pixel-adaptive convolution (PAC) operation, a simple yet effective modification of standard convolutions, in which the filter weights are multiplied with a spatially-varying kernel that depends on learnable, local pixel features. PAC is a generalization of several popular filtering techniques and thus can be used for a wide range of use cases. Specifically, we demonstrate state-of-the-art performance when PAC is used for deep joint image upsampling.

This paper describes a short-reach serial link to connect chips mounted on the same package or on neighboring packages on a printed circuit board (PCB). The link employs an energy-efficient, single-ended ground-referenced signaling scheme. Implemented in 16-nm FinFET CMOS technology, the link operates at a data rate of 25 Gb/s/pin with 1.17-pJ/bit energy efficiency and uses a simple but robust matched-delay clock forwarding scheme that cancels most sources of jitter.

This work presents a scalable deep neural network (DNN) accelerator consisting of 36 chips connected in a mesh network on a multi-chip-module (MCM) using ground-referenced signaling (GRS). While previous accelerators fabricated on a single monolithic die are limited to specific network sizes, the proposed architecture enables flexible scaling for efficient inference on a wide range of DNNs, from mobile to data center domains.

Analog/mixed-signal (AMS) computation can be more energy efficient than digital approaches for deep learning inference, but incurs an accuracy penalty from precision loss. Prior AMS approaches focus on small networks/datasets, which can maintain accuracy even with 2b precision. We analyze applicability of AMS approaches to larger networks by proposing a generic AMS error model, implementing it in an existing training framework, and investigating its effect on ImageNet classification with ResNet-50.

This paper introduces PRIMAL, a novel learning-based framework that enables fast and accurate power estimation for ASIC designs. PRIMAL trains machine learning (ML) models with design verification testbenches for characterizing the power of reusable circuit building blocks. The trained models can then be used to generate detailed power profiles of the same blocks under different workloads. We evaluate the performance of several established ML models on this task, including ridge regression, gradient tree boosting, multi-layer perceptron, and convolutional neural network (CNN).

Applications of deep learning to electronic design automation (EDA) have recently begun to emerge, although they have mainly been limited to processing of regular structured data such as images. However, many EDA problems require processing irregular structures, and it can be non-trivial to manually extract important features in such cases. In this paper, a high performance graph convolutional network (GCN) model is proposed for the purpose of processing irregular graph representations of logic circuits. A GCN classifier is firstly trained to predict observation point candidates in a netlist.

Virtual and augmented reality (VR/AR) are expected to revolutionise entertainment, healthcare, communication and the manufac-turing industries among many others. Near-eye displays are an enabling vessel for VR/AR applications, which have to tacklemany challenges related to ergonomics, comfort, visual quality and natural interaction. These challenges are related to thecore elements of these near-eye display hardware and tracking technologies.

We describe simple methods for how to filter environment maps using ray cones and ray differentials in a ray tracing engine.

Unlike rasterization, where one can rely on pixel quad partial derivatives, an alternative approach must be taken for filtered texturing during ray tracing. We describe two methods for computing texture level of detail for ray tracing. The first approach uses ray differentials, which is a general solution that gives high-quality results. It is rather expensive in terms of computations and ray storage, however. The second method builds on ray cone tracing and uses a single trilinear lookup, a small amount of ray storage, and fewer computations than ray differentials.