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Tiny machine learning (TinyML), executing AI workloads on resource and power strictly restricted systems, is an important and challenging topic. This brief firstly presents an extremely tiny backbone to construct high efficiency CNN models…

A three-dimensional (3D) Network-on-Chip (NoC) enables the design of high performance and low power many-core chips. Existing 3D NoCs are inadequate for meeting the ever-increasing performance requirements of many-core processors since they…

Efficient on-device neural network (NN) inference offers predictable latency, improved privacy and reliability, and lower operating costs for vendors than cloud-based inference. This has sparked recent development of microcontroller-scale…

Energy minimization has been an intensely studied core problem in computer vision. With growing image sizes (2D and 3D), it is now highly desirable to run energy minimization algorithms in parallel. But many existing algorithms, in…

The recent push for post-Moore computer architectures has introduced a wide variety of application-specific accelerators. One particular accelerator, the resistance network analogue, has been well received due to its ability to efficiently…

In light of the ever growing energy needs of the ICT sector, a value that is becoming increasingly important for a mobile network is its power consumption. However, the transition away from legacy network deployments tightly coupled with…

Any architecture for practical quantum computing must be scalable. An attractive approach is to create multiple cores, computing regions of fixed size that are well-spaced but interlinked with communication channels. This exploded…

In this paper we accomplish the development of the fast rank-adaptive solver for tensor-structured symmetric positive definite linear systems in higher dimensions. In [arXiv:1301.6068] this problem is approached by alternating minimization…

Neuromorphic computing with non-volatile memory (NVM) can significantly improve performance and lower energy consumption of machine learning tasks implemented using spike-based computations and bio-inspired learning algorithms. High…

Battery-less technology evolved to replace battery technology. Non-volatile memory (NVM) based processors were explored to store the program state during a power failure. The energy stored in a capacitor is used for a backup during a power…

As neural networks (NN) are deployed across diverse sectors, their energy demand correspondingly grows. While several prior works have focused on reducing energy consumption during training, the continuous operation of ML-powered systems…

The widespread adoption of data-centric algorithms, particularly Artificial Intelligence (AI) and Machine Learning (ML), has exposed the limitations of centralized processing infrastructures, driving a shift towards edge computing. This…

Non-volatile memory (NVM) technologies are interesting alternatives for building the on-chip Last-Level Cache (LLC). Their advantages, compared to SRAM memory, are higher density and lower static power, but each write operation slightly…

The demand for computation resources and energy efficiency of Convolutional Neural Networks (CNN) applications requires a new paradigm to overcome the "Memory Wall". Analog In-Memory Computing (AIMC) is a promising paradigm since it…

Much work has been dedicated to estimating and optimizing workloads in high-performance computing (HPC) and deep learning. However, researchers have typically relied on few metrics to assess the efficiency of those techniques. Most notably,…

Electrical power requirements will be a constraint on the future growth of Distributed High Throughput Computing (DHTC) as used by High Energy Physics. Performance-per-watt is a critical metric for the evaluation of computer architectures…

Due to the high activation sparsity and use of accumulates (AC) instead of expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have emerged as a promising low-power alternative to traditional DNNs for…

Decades of exponential scaling in high performance computing (HPC) efficiency is coming to an end. Transistor based logic in complementary metal-oxide semiconductor (CMOS) technology is approaching physical limits beyond which further…

Existing power modelling research focuses not on the method used for developing models but rather on the model itself. This paper aims to develop a method for deploying power models on emerging processors that will be used, for example, in…

We provide a framework for optimizing energy conversion processes in coherent quantum conductors fed by nonthermal resources. Such nonthermal resources, which cannot be characterized by temperatures or electrochemical potentials, occur in…