221 citations · 357 across the 24 of their papers we have counts for
6 papers · 1 filter
An Iterated Hybrid Fast Parallel FIR Filter
Keshab K. Parhi
This paper revisits the design and optimization of parallel fast finite impulse response (FIR) filters using polyphase decomposition and iterated fast FIR algorithms (FFAs). Parall…
The Equivalence of Fast Algorithms for Convolution, Parallel FIR Filters, Polynomial Modular Multiplication, and Pointwise Multiplication in DFT/NTT Domain
Keshab K. Parhi
Fast time-domain algorithms have been developed in signal processing applications to reduce the multiplication complexity. For example, fast convolution structures using Cook-Toom…
SpikePipe: Accelerated Training of Spiking Neural Networks via Inter-Layer Pipelining and Multiprocessor Scheduling
Sai Sanjeet, Bibhu Datta Sahoo, Keshab K. Parhi
Spiking Neural Networks (SNNs) have gained popularity due to their high energy efficiency. Prior works have proposed various methods for training SNNs, including backpropagation-ba…
Multi-Channel FFT Architectures Designed via Folding and Interleaving
Nanda K. Unnikrishnan, Keshab K. Parhi
Computing the FFT of a single channel is well understood in the literature. However, computing the FFT of multiple channels in a systematic manner has not been fully addressed. Thi…
Teaching Digital Signal Processing by Partial Flipping, Active Learning and Visualization
Keshab K. Parhi
Effectiveness of teaching digital signal processing can be enhanced by reducing lecture time devoted to theory, and increasing emphasis on applications, programming aspects, visual…
A Gradient-Interleaved Scheduler for Energy-Efficient Backpropagation for Training Neural Networks
Nanda Unnikrishnan, Keshab K. Parhi
This paper addresses design of accelerators using systolic architectures for training of neural networks using a novel gradient interleaving approach. Training the neural network i…