Map Space Exploration is the problem of finding optimized mappings of a Deep Neural Network (DNN) model on an accelerator. It is known to be extremely computationally expensive, and there has been active research looking at both heuristics and learning-based methods to make the problem computationally tractable.

To meet the extreme compute demands for deep learning across commercial and scientific applications, dataflow accelerators are becoming increasingly popular. While these “domain-specific” accelerators are not fully programmable like CPUs and GPUs, they retain varying levels of flexibility with respect to data orchestration, i.e., dataflow and tiling optimizations to enhance efficiency. There are several challenges when designing new algorithms and mapping approaches to execute the algorithms for a target problem on new hardware. Previous works have addressed these challenges individually.

Sparse deep neural network (DNN) accelerators exploit the intrinsic redundancy in data representation to achieve high performance and energy efficiency. However, sparse weight and input activation arrays are unstructured, and their processing cannot take advantage of the regular data-access patterns offered by dense arrays, thus the processing incurs increased complexities in dataflow orchestra- tion and resource management.

Cloud rendering is attractive when targeting thin client devices such as phones or VR/AR headsets, or any situation where a high-end GPU is not available due to thermal or power constraints. However, it introduces the challenge of streaming rendered data over a network in a manner that is robust to latency and potential dropouts. Current approaches range from streaming transmitted video and correcting it on the client---which fails in the presence of disocclusion events---to solutions where the server sends geometry and all rendering is performed on the client.

Creating 3D shapes from 2D drawings is an important problem with applications in content creation for computer animation and virtual reality. We introduce a new sketch-based system, CreatureShop, that enables amateurs to create high-quality textured 3D character models from 2D drawings with ease and efficiency.

The hardware design space is high-dimensional and discrete. Systematic and efficient exploration of this space has been a significant challenge. Central to this problem is the intractable search complexity that grows exponentially with the design choices and the discrete nature of the search space. This work investigates the feasibility of learning a meaningful low-dimensional continuous representation for hardware designs to reduce such complexity and facilitate the search process.

Unlike cache-based load-store architectures, Explicit De- coupled Data Orchestration (EDDO) architectures are pro- grammed using decoupled but synchronized programs run- ning at various units on the hardware, moving data between storage units and/or performing computations. As such, they present a unique programming challenge.

This paper presents Timeloop, an infrastructure for evaluating and exploring the architecture design space of deep neural network (DNN) accelerators. Timeloop uses a concise and unified representation of the key architecture and implementation attributes of DNN accelerators to describe a broad space of hardware topologies. It can then emulate those topologies to generate an accurate projection of performance and energy efficiency for a DNN workload through a mapper that finds the best way to schedule operations and stage data on the specified architecture.

As CNNs are increasingly being employed in high performance computing and safety-critical applications, ensuring they are reliable to transient hardware errors is important. Full duplication provides high reliability, but the overheads are prohibitively high for resource constrained systems. Fine-grained resilience evaluation and protection can provide a low-cost solution, but traditional methods for evaluation can be too slow. Traditional approaches use error injections and essentially discard information from experiments that do not corrupt outcomes.

DRAM is the primary technology used for main memory in modern systems. Unfortunately, as DRAM scales down to smaller technology nodes, it faces key challenges in both data integrity and latency, which strongly affects overall system reliability and performance. To develop reliable and high-performance DRAM-based main memory in future systems, it is critical to characterize, understand, and analyze various aspects (e.g., reliability, latency) of existing DRAM chips.