Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture
Zishen Wan, Che-Kai Liu, Hanchen Yang et al. · 1 Sep 2024
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, limited robustness, and a lack of explainability. To develop next-generation cognitive AI systems, neuro-symbolic AI emerges as a promising paradigm, fusing neural and symbolic approaches to enhance interpretability, robustness, and trustworthiness, while facilitating learning from…
EvGNN: An Event-Driven Graph Neural Network Accelerator for Edge Vision
Yufeng Yang, Adrian Kneip, Charlotte Frenkel · 23 Dec 2024
Edge vision systems combining sensing and embedded processing promise low-latency, decentralized, and energy-efficient solutions that forgo reliance on the cloud. As opposed to conventional frame-based vision sensors, event-based cameras deliver a microsecond-scale temporal resolution with sparse information encoding, thereby outlining new opportunities for edge vision systems. However, mainstream algorithms for frame-based vision, which mostly rely…
FPGA-Based Adaptive LIF Neuron for High-Speed, Energy-Efficient Spiking Neural Network
Saras Mani Mishra, Hanumant Singh Shekhawat, Jan Pidanič et al. · 8 May 2025
Spiking Neural Networks (SNNs) have drawn attention due to their biological behaviour and low energy consumption compared to conventional neural networks. The efficiency of SNN depends on the neurons, and thus, proper selection of neurons becomes imperative for exhibiting appropriate biological behaviour. This paper realizes an adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Leaky Integrate-and-Fire</i> (LIF) neuron, modeled using…
Neuro-Photonix: Enabling Near-Sensor Neuro-Symbolic AI Computing on Silicon Photonics Substrate
Deniz Najafi, Hamza Errahmouni Barkam, Mehrdad Morsali et al. · 4 Feb 2025
Neuro-symbolic Artificial Intelligence (AI) models, blending neural networks with symbolic AI, have facilitated transparent reasoning and context understanding without the need for explicit rule-based programming. However, implementing such models in the Internet of Things (IoT) sensor nodes presents hurdles due to computational constraints and intricacies. In this work, for the first time, we propose a…
DeltaKWS: A 65nm 36nJ/Decision Bio-Inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC With 0.6V Near-Threshold SRAM
Qinyu Chen, Kwantae Kim, Chang Gao et al. · 28 Nov 2024
This paper introduces DeltaKWS, which is to the best of our knowledge, the first <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Delta$</tex-math></inline-formula>RNN-enabled, fine-grained, temporal sparsity-aware Keyword Spotting (KWS) integrated circuit (IC) designed for voice-controlled devices. The 65 nm prototype chip features a number of techniques to enhance performance, area, and power efficiencies, specifically: 1) a bio-inspired delta-gated recurrent neural…
