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ACM/IEEE 44th Annual International Symposium on…
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… Continue Reading
49th Annual IEEE/ACM International Symposium on…
The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction… Continue Reading
IEEE 21st International Symposium on High…
GPUs employ massive multithreading and fast context switching to provide high throughput and hide memory latency. Multithreading can Increase contention for various system resources, however, that… Continue Reading
IEEE International Symposium on High Performance…
Popular deep learning frameworks require users to fine-tune their memory usage so that the training data of a deep neural network (DNN) fits within the GPU physical memory. Prior work tries to… Continue Reading
46th Annual IEEE/ACM International Symposium on…
As GPU's compute capabilities grow, their memory hierarchy increasingly becomes a bottleneck. Current GPU memory hierarchies use coarse-grained memory accesses to exploit spatial locality, maximize… Continue Reading
IEEE 19th International Symposium on High…
Current graphics processing units (GPUs) utilize the single instruction multiple thread (SIMT) execution model. With SIMT, a group of logical threads executes such that all threads in the group… Continue Reading
IEEE International Symposium on High Performance…
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… Continue Reading
39th Annual International Symposium on Computer…
Wide SIMD-based GPUs have evolved into a promising platform for running general purpose workloads. Current programmable GPUs allow even code with irregular control to execute well on their SIMD… Continue Reading
IEEE Transactions on Circuits and Systems for…
Embedded block coding with optimized truncation (EBCOT) employed in the JPEG2000 standard accounts for the majority of the processing time, because the EBCOT is full of bit operations that cannot be… Continue Reading
Recent studies from several hyperscalars pinpoint to embedding layers as the most memory-intensive deep learning (DL) algorithm being deployed in today's datacenters. This paper addresses the memory… Continue Reading