Zobrazeno 1 - 10
of 1 525
pro vyhledávání: '"Parhi, P."'
Spiking Neural Networks (SNNs) have gained popularity due to their high energy efficiency. Prior works have proposed various methods for training SNNs, including backpropagation-based methods. Training SNNs is computationally expensive compared to th
Externí odkaz:
http://arxiv.org/abs/2406.06879
We consider a large class of shallow neural networks with randomly initialized parameters and rectified linear unit activation functions. We prove that these random neural networks are well-defined non-Gaussian processes. As a by-product, we demonstr
Externí odkaz:
http://arxiv.org/abs/2405.10229
Autor:
Ge, Lulu, Parhi, Keshab K.
Publikováno v:
IEEE Open Journal of Circuits and Systems, Vol. 5, pp. 102-116, 2024
This paper addresses the clustering of data in the hyperdimensional computing (HDC) domain. In prior work, an HDC-based clustering framework, referred to as HDCluster, has been proposed. However, the performance of the existing HDCluster is not robus
Externí odkaz:
http://arxiv.org/abs/2312.02407
Publikováno v:
2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)
CRYSTAL-Kyber (Kyber) is one of the post-quantum cryptography (PQC) key-encapsulation mechanism (KEM) schemes selected during the standardization process. This paper addresses optimization for Kyber architecture with respect to latency and throughput
Externí odkaz:
http://arxiv.org/abs/2310.04618
Autor:
Parhi, Rahul, Unser, Michael
We investigate the function-space optimality (specifically, the Banach-space optimality) of a large class of shallow neural architectures with multivariate nonlinearities/activation functions. To that end, we construct a new family of Banach spaces d
Externí odkaz:
http://arxiv.org/abs/2310.03696
Autor:
Parhi, Rahul, Unser, Michael
Publikováno v:
SIAM Journal on Mathematical Analysis, vol. 56, no. 4, pp. 4662-4686, 2024
We investigate the distributional extension of the $k$-plane transform in $\mathbb{R}^d$ and of related operators. We parameterize the $k$-plane domain as the Cartesian product of the Stiefel manifold of orthonormal $k$-frames in $\mathbb{R}^d$ with
Externí odkaz:
http://arxiv.org/abs/2310.01233
Autor:
Mondal, Arijit, Parhi, Keshab K.
Publikováno v:
IEEE Circuits and Systems Magazine, 24(1), pp. 33-51, 2024
Quantum computers have the potential to provide exponential speedups over their classical counterparts. Quantum principles are being applied to fields such as communications, information processing, and artificial intelligence to achieve quantum adva
Externí odkaz:
http://arxiv.org/abs/2309.11793
Autor:
Mondal, Arijit, Parhi, Keshab K.
Publikováno v:
IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 72, 2024
Quantum computing is an emerging technology that has the potential to achieve exponential speedups over their classical counterparts. To achieve quantum advantage, quantum principles are being applied to fields such as communications, information pro
Externí odkaz:
http://arxiv.org/abs/2309.12373
Autor:
Parhi, Keshab K.
Publikováno v:
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, April 2024
This paper addresses the design of a partly-parallel cascaded FFT-IFFT architecture that does not require any intermediate buffer. Folding can be used to design partly-parallel architectures for FFT and IFFT. While many cascaded FFT-IFFT architecture
Externí odkaz:
http://arxiv.org/abs/2309.09035
We investigate the approximation of functions $f$ on a bounded domain $\Omega\subset \mathbb{R}^d$ by the outputs of single-hidden-layer ReLU neural networks of width $n$. This form of nonlinear $n$-term dictionary approximation has been intensely st
Externí odkaz:
http://arxiv.org/abs/2307.15772