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To overcome the burden on the memory size and bandwidth due to ever-increasing size of large language models (LLMs), aggressive weight quantization has been recently studied, while lacking research on quantizing activations. In this paper, we present a ...

This paper proposes a novel photo storage system called NDPipe, which accelerates the performance of training and inference for image data by leveraging near-data processing in photo storage servers. NDPipe distributes storage servers with inexpensive ...

When training deep neural networks (DNNs), expensive floating point arithmetic units are used in GPUs or custom neural processing units (NPUs). To reduce the burden of floating point arithmetic, community has started exploring the use of more efficient ...

Recent advances in deep learning have made it possible to implement artificial intelligence in mobile devices. Many studies have put a lot of effort into developing lightweight deep learning models optimized for mobile devices. To overcome the performance ...

This article discusses the high-performance near-memory neural network (NN) accelerator architecture utilizing the logic die in three-dimensional (3D) High Bandwidth Memory– (HBM) like memory. As most of the previously reported 3D memory-based near-memory ...

In this paper, we present an integrated solution to design a high-performance LSTM accelerator. We propose a fast and flexible hardware architecture, named Peregrine, supported by a stack of innovations from algorithm to hardware design. Peregrine first ...

Memory performance is a key bottleneck for deep learning systems. Binarization of both activations and weights is one promising approach that can best scale to realize the highest energy efficient system using the lowest possible precision. In this ...

The fast and energy-efficient simulation of dynamical systems defined by coupled ordinary/partial differential equations has emerged as an important problem. The accelerated simulation of coupled ODE/PDE is critical for analysis of physical systems as ...

Neural networks generally require significant memory capacity/bandwidth to store/access a large number of synaptic weights. This paper presents an application of JPEG image encoding to compress the weights by exploiting the spatial locality and ...

This paper presents methodology of feedback-controlled dynamic approximation to enable energy-accuracy trade-off in digital recurrent neural network (RNN). A low-power digital RNN engine is presented that employs the proposed dynamic approximation. The ...

This paper presents a programmable and scalable digital neuromorphic architecture based on 3D high-density memory integrated with logic tier for efficient neural computing. The proposed architecture consists of clusters of processing engines, connected ...

This paper studies the opportunities of energy-accuracy tradeoff in cellular neural network (CNN). Algorithmic characteristics of CNN is coupled with hardware-induced error distribution of a digital CNN cell to evaluate energy-accuracy tradeoff for simple ...

A floorplanning has a potential to reduce chip temperature due to the conductive nature of heat. If floorplan optimization, which is usually based on simulated annealing, is employed to reduce temperature, its evaluation should be done extremely fast ...