category: AI/ML/DL

category: IoT, Security & Automotive

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices
By Technical Paper Link - 20 Dec, 2021 - Comments: 0

Abstract:  "Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and energy-efficient hardware accelerators. We study the potential of Analog AI accelerators based on Non-Volatile Memory, in particular Phase Change Memory (PCM), for software-equivalent accurate i... » read more

Benchmarking Highly Parallel Hardware for Spiking Neural Networks in Robotics
By Technical Paper Link - 20 Dec, 2021 - Comments: 0

Abstract: "Animal brains still outperform even the most performant machines with significantly lower speed. Nonetheless, impressive progress has been made in robotics in the areas of vision, motion- and path planning in the last decades. Brain-inspired Spiking Neural Networks (SNN) and the parallel hardware necessary to exploit their full potential have promising features for robotic applica... » read more

Absence of Barren Plateaus in Quantum Convolutional Neural Networks
By Technical Paper Link - 18 Nov, 2021 - Comments: 0

Abstract:  Quantum neural networks (QNNs) have generated excitement around the possibility of efficiently analyzing quantum data. But this excitement has been tempered by the existence of exponentially vanishing gradients, known as barren plateau landscapes, for many QNN architectures. Recently, quantum convolutional neural networks (QCNNs) have been proposed, involving a sequence of convol... » read more

Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware
By Technical Paper Link - 18 Nov, 2021 - Comments: 0

Abstract: "Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuromorphic platforms that leverage the complementary properties of optics and electronics. Starting from the conjecture that future large-scale neurom... » read more

Enabling Training of Neural Networks on Noisy Hardware
By Technical Paper Link - 18 Nov, 2021 - Comments: 0

Abstract:  "Deep neural networks (DNNs) are typically trained using the conventional stochastic gradient descent (SGD) algorithm. However, SGD performs poorly when applied to train networks on non-ideal analog hardware composed of resistive device arrays with non-symmetric conductance modulation characteristics. Recently we proposed a new algorithm, the Tiki-Taka algorithm, that overcomes t... » read more

Accelerating Inference of Convolutional Neural Networks Using In-memory Computing
By Technical Paper Link - 17 Nov, 2021 - Comments: 0

Abstract: "In-memory computing (IMC) is a non-von Neumann paradigm that has recently established itself as a promising approach for energy-efficient, high throughput hardware for deep learning applications. One prominent application of IMC is that of performing matrix-vector multiplication in (1) time complexity by mapping the synaptic weights of a neural-network layer to the devices of a... » read more

A graph placement methodology for fast chip design
By Technical Paper Link - 09 Nov, 2021 - Comments: 0

Abstract "Chip floorplanning is the engineering task of designing the physical layout of a computer chip. Despite five decades of research1, chip floorplanning has defied automation, requiring months of intense effort by physical design engineers to produce manufacturable layouts. Here we present a deep reinforcement learning approach to chip floorplanning. In under six hours, our method autom... » read more

Accelerating Inference of Convolutional Neural Networks Using In-memory Computing
By Technical Paper Link - 09 Nov, 2021 - Comments: 0

Abstract: "In-memory computing (IMC) is a non-von Neumann paradigm that has recently established itself as a promising approach for energy-efficient, high throughput hardware for deep learning applications. One prominent application of IMC is that of performing matrix-vector multiplication in (1) time complexity by mapping the synaptic weights of a neural-network layer to the devices of an ... » read more

Neuromorphic electronics based on copying and pasting the brain
By Technical Paper Link - 09 Nov, 2021 - Comments: 1

Abstract: "Reverse engineering the brain by mimicking the structure and function of neuronal networks on a silicon integrated circuit was the original goal of neuromorphic engineering, but remains a distant prospect. The focus of neuromorphic engineering has thus been relaxed from rigorous brain mimicry to designs inspired by qualitative features of the brain, including event-driven sign... » read more

Energy-efficient memcapacitor devices for neuromorphic computing
By Technical Paper Link - 03 Nov, 2021 - Comments: 0

Abstract Data-intensive computing operations, such as training neural networks, are essential for applications in artificial intelligence but are energy intensive. One solution is to develop specialized hardware onto which neural networks can be directly mapped, and arrays of memristive devices can, for example, be trained to enable parallel multiply–accumulate operations. Here we show that ... » read more

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