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Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Accelerating Convolutional Neural Networks (CNNs) on GPUs usually involves two stages: training and inference. Traditionally, this two-stage process is deployed on high-end GPU-equipped servers. Driven by the increase in compute power of desktop and mobile GPUs, there is growing interest in performing inference on various kinds of platforms. In contrast to the requirements of high throughput and accuracy...
The memory wall continues to be a major performance bottleneck. While small on-die caches have been effective so far in hiding this bottleneck, the ever-increasing footprint of modern applications renders such caches ineffective. Recent advances in memory technologies like embedded DRAM (eDRAM) and High Bandwidth Memory (HBM) have enabled the integration of large memories on the CPU package as an...
The rate of network packets encapsulating requests from clients can significantly affect the utilization, and thus performance and sleep states of processors in servers deploying a power management policy. To improve energy efficiency, servers may adopt an aggressive power management policy that frequently transitions a processor to a low-performance or sleep state at a low utilization. However, such...
While emerging accelerator-centric architectures offer orders-of-magnitude performance and energy improvements, use cases and adoption can be limited by their rigid programming model. A unified virtual address space between the host CPU cores and customized accelerators can largely improve the programmability, which necessitates hardware support for address translation. However, supporting address...
Modern NAND flash memory chips provide high density by storing two bits of data in each flash cell, called a multi-level cell (MLC). An MLC partitions the threshold voltage range of a flash cell into four voltage states. When a flash cell is programmed, a high voltage is applied to the cell. Due to parasitic capacitance coupling between flash cells that are physically close to each other, flash cell...
Technology scaling has continuously improved the density, performance, energy efficiency, and cost of DRAM-based main memory systems. Starting from sub-20nm processes, however, the industry began to pay considerably higher costs to screen and manage notably increasing defective cells. The traditional technique, which replaces the rows/columns containing faulty cells with spare rows/columns, has been...
This paper proposes an energy-efficient, high-throughput DRAM architecture for GPUs and throughput processors. In these systems, requests from thousands of concurrent threads compete for a limited number of DRAM row buffers. As a result, only a fraction of the data fetched into a row buffer is used, leading to significant energy overheads. Our proposed DRAM architecture exploits the hierarchical organization...
The challenges to push computing to exaflop levels are difficult given desired targets for memory capacity, memory bandwidth, power efficiency, reliability, and cost. This paper presents a vision for an architecture that can be used to construct exascale systems. We describe a conceptual Exascale Node Architecture (ENA), which is the computational building block for an exascale supercomputer. The...
Defining a processor micro-architecture for a targeted productspace involves multi-dimensional optimization across performance, power and reliability axes. A key decision in sucha definition process is the circuit-and technology-driven parameterof the nominal (voltage, frequency) operating point. This is a challenging task, since optimizing individually orpair-wise amongst these metrics usually results...
Much prior work has studied cache replacement, but a large gap remains between theory and practice. The design of many practical policies is guided by the optimal policy, Belady's MIN. However, MIN assumes perfect knowledge of the future that is unavailable in practice, and the obvious generalizationsof MIN are suboptimal with imperfect information. What, then, is the right metric for practical cache...
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