Animesh Jain

TL;DR: This paper investigates widely used DNNs and finds that the major contributors to memory footprint are intermediate layer outputs (feature maps), and introduces a framework for DNN-layer-specific optimizations that significantly reduce this source of main memory pressure on GPUs.

TL;DR: DeftNN is a GPU DNN execution framework that targets the key architectural bottlenecks of DNNs on GPUs to automatically and transparently improve execution performance, and is composed of two novel optimization techniques– synapse vector elimination, a technique that identifies non-contributing synapses in the DNN and carefully transforms data and removes the computation and data movement.

TL;DR: In this paper, a single-cylinder diesel engine with and without magnetic fuel conditioning at various locations on fuel line was tested with a 0.5% CuO (wt/wt) mass fraction nanoparticles and diesel fuel.

TL;DR: This paper introduces a novel approximate computing technique that decouples the format of data in the memory hierarchy from the format in the compute subsystem to significantly reduce the cost of storing and moving bits throughout theMemory hierarchy and improve application performance.

TL;DR: This paper addresses the challenges of executing quantized deep learning models on diverse hardware platforms by proposing an augmented compiler approach that created a new dialect called Quantized Neural Network (QNN) that extends the compiler's internal representation with a quantization context.