Bit Error Robustness for Energy-Efficient DNN Accelerators
David Stutz, Nandhini Chandramoorthy, et al.
MLSys 2021
Neuro-symbolic AI approaches display both perception and reasoning capabilities, but inherit the limitations of their individual deep learning and symbolic AI components. By combining neural networks and vector-symbolic architecture machinery, we propose the concept of neuro-vector-symbolic architecture (NVSA). NVSA solves few-shot continual learning, visual abstract reasoning, and computationally hard problems such as factorization faster and more accurately than other state-of-the-art methods. We also show how the efficient realization of NVSA can be informed and benefitted by the physical properties of in-memory computing hardware, e.g., O(1) MVM, in-situ progressive crystallization, and intrinsic stochasticity of phase-change memory devices.
David Stutz, Nandhini Chandramoorthy, et al.
MLSys 2021
Juan Miguel De Haro, Rubén Cano, et al.
IPDPS 2022
Eric A. Joseph
AVS 2023
Stefano Ambrogio
MRS Spring Meeting 2022