84 citations · 223 across the 25 of their papers we have counts for
3 papers · 1 filter
Doing More by Doing Less: How Structured Partial Backpropagation Improves Deep Learning Clusters
Adarsh Kumar, Kausik Subramanian, Shivaram Venkataraman +1
Many organizations employ compute clusters equipped with accelerators such as GPUs and TPUs for training deep learning models in a distributed fashion. Training is resource-intensi…
PL2: Towards Predictable Low Latency in Rack-Scale Networks
Yanfang Le, Radhika Niranjan Mysore, Lalith Suresh +4
High performance rack-scale offerings package disaggregated pools of compute, memory and storage hardware in a single rack to run diverse workloads with varying requirements, inclu…
Accelerating Deep Learning Inference via Learned Caches
Arjun Balasubramanian, Adarsh Kumar, Yuhan Liu +3
Deep Neural Networks (DNNs) are witnessing increased adoption in multiple domains owing to their high accuracy in solving real-world problems. However, this high accuracy has been…