1.4 The Future of Computing: Bits + Neurons + Qubits

Gil, Darı́o; Green, William M. J.

doi:10.1109/isscc19947.2020.9062918

Cited by 14 publications

(5 citation statements)

References 55 publications

(58 reference statements)

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“…Although digital implementations are widely used for neuromorphic systems, their performance is limited in the current machine learning applications. Analog computation paves the way to achieve Tera operations per second per watt efficiency which is 100x compared to digital implementations ( Gil and Green, 2020 ). Various types of analog devices have been used to implement neural networks, such as CMOS transistors ( Indiveri et al, 2011 ), floating gate transistors ( Kim et al, 2018a ), gated Schottky diode ( Kwon et al, 2020 ), and emerging memory devices (like PCM, RRAM, STTRAM) ( Kim et al, 2018b ).…”

Section: Resultsmentioning

confidence: 99%

Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems

et al. 2021

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Various hypotheses of information representation in brain, referred to as neural codes, have been proposed to explain the information transmission between neurons. Neural coding plays an essential role in enabling the brain-inspired spiking neural networks (SNNs) to perform different tasks. To search for the best coding scheme, we performed an extensive comparative study on the impact and performance of four important neural coding schemes, namely, rate coding, time-to-first spike (TTFS) coding, phase coding, and burst coding. The comparative study was carried out using a biological 2-layer SNN trained with an unsupervised spike-timing-dependent plasticity (STDP) algorithm. Various aspects of network performance were considered, including classification accuracy, processing latency, synaptic operations (SOPs), hardware implementation, network compression efficacy, input and synaptic noise resilience, and synaptic fault tolerance. The classification tasks on Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets were applied in our study. For hardware implementation, area and power consumption were estimated for these coding schemes, and the network compression efficacy was analyzed using pruning and quantization techniques. Different types of input noise and noise variations in the datasets were considered and applied. Furthermore, the robustness of each coding scheme to the non-ideality-induced synaptic noise and fault in analog neuromorphic systems was studied and compared. Our results show that TTFS coding is the best choice in achieving the highest computational performance with very low hardware implementation overhead. TTFS coding requires 4x/7.5x lower processing latency and 3.5x/6.5x fewer SOPs than rate coding during the training/inference process. Phase coding is the most resilient scheme to input noise. Burst coding offers the highest network compression efficacy and the best overall robustness to hardware non-idealities for both training and inference processes. The study presented in this paper reveals the design space created by the choice of each coding scheme, allowing designers to frame each scheme in terms of its strength and weakness given a designs’ constraints and considerations in neuromorphic systems.

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Section: Resultsmentioning

confidence: 99%

Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems

et al. 2021

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“…Advancing from narrow AI to broad AI will only become possible once quantum processors are engaged in complex AI systems that requires a lot of complex computations. Also, AI will operate in multiple domains and will learn from small quantities of multi-modal input data [32].…”

Section: Outlinementioning

confidence: 99%

Quantum Internet- Applications, Functionalities, Enabling Technologies, Challenges, and Research Directions

Singh¹,

Dev²,

Šiljak³

et al. 2021

Preprint

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“…Gil and Green denote the three concepts as bit, neuron, and qubit, respectively. As shown in Figure 4, the next-generation AI-enabled computing system requires integration of the three [117]. In this figure, we adopt quantum computing (qubits) to represent future computing paradigms.…”

Section: Systematic Integrationmentioning

confidence: 99%

Deep Learning on Computational-Resource-Limited Platforms: A Survey

Chen

Zhang

et al. 2020

Mobile Information Systems

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Nowadays, Internet of Things (IoT) gives rise to a huge amount of data. IoT nodes equipped with smart sensors can immediately extract meaningful knowledge from the data through machine learning technologies. Deep learning (DL) is constantly contributing significant progress in smart sensing due to its dramatic superiorities over traditional machine learning. The promising prospect of wide-range applications puts forwards demands on the ubiquitous deployment of DL under various contexts. As a result, performing DL on mobile or embedded platforms is becoming a common requirement. Nevertheless, a typical DL application can easily exhaust an embedded or mobile device owing to a large amount of multiply and accumulate (MAC) operations and memory access operations. Consequently, it is a challenging task to bridge the gap between deep learning and resource-limited platforms. We summarize typical applications of resource-limited deep learning and point out that deep learning is an indispensable impetus of pervasive computing. Subsequently, we explore the underlying reasons for the high computational overhead of DL through reviewing the fundamental concepts including capacity, generalization, and backpropagation of a neural network. Guided by these concepts, we investigate on principles of representative research works, as well as three types of solutions: algorithmic design, computational optimization, and hardware revolution. In pursuant to these solutions, we identify challenges to be addressed.

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1.4 The Future of Computing: Bits + Neurons + Qubits

Cited by 14 publications

References 55 publications

Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems

Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems

Quantum Internet- Applications, Functionalities, Enabling Technologies, Challenges, and Research Directions

Deep Learning on Computational-Resource-Limited Platforms: A Survey

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