294 citations · 641 across the 22 of their papers we have counts for
24 papers
On the Role of Noise in Factorizers for Disentangling Distributed Representations
Geethan Karunaratne, Michael Hersche, Abu Sebastian +1
To efficiently factorize high-dimensional distributed representations to the constituent atomic vectors, one can exploit the compute-in-superposition capabilities of vector-symboli…
Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift Generalization
Gentiana Rashiti, Geethan Karunaratne, Mrinmaya Sachan +2
The retrieval augmented generation (RAG) system such as Retro has been shown to improve language modeling capabilities and reduce toxicity and hallucinations by retrieving from a d…
Zero-shot Classification using Hyperdimensional Computing
Samuele Ruffino, Geethan Karunaratne, Michael Hersche +3
Classification based on Zero-shot Learning (ZSL) is the ability of a model to classify inputs into novel classes on which the model has not previously seen any training examples. P…
TCNCA: Temporal Convolution Network with Chunked Attention for Scalable Sequence Processing
Aleksandar Terzic, Michael Hersche, Geethan Karunaratne +3
MEGA is a recent transformer-based architecture, which utilizes a linear recurrent operator whose parallel computation, based on the FFT, scales as , with being the s…
MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition
Nicolas Menet, Michael Hersche, Geethan Karunaratne +3
With the advent of deep learning, progressively larger neural networks have been designed to solve complex tasks. We take advantage of these capacity-rich models to lower the cost…
Factorizers for Distributed Sparse Block Codes
Michael Hersche, Aleksandar Terzic, Geethan Karunaratne +6
Distributed sparse block codes (SBCs) exhibit compact representations for encoding and manipulating symbolic data structures using fixed-width vectors. One major challenge however…
WHYPE: A Scale-Out Architecture with Wireless Over-the-Air Majority for Scalable In-memory Hyperdimensional Computing
Robert Guirado, Abbas Rahimi, Geethan Karunaratne +3
Hyperdimensional computing (HDC) is an emerging computing paradigm that represents, manipulates, and communicates data using long random vectors known as hypervectors. Among differ…
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
Manuel Le Gallo, Riduan Khaddam-Aljameh, Milos Stanisavljevic +26
The need to repeatedly shuttle around synaptic weight values from memory to processing units has been a key source of energy inefficiency associated with hardware implementation of…
In-memory factorization of holographic perceptual representations
Jovin Langenegger, Geethan Karunaratne, Michael Hersche +3
Disentanglement of constituent factors of a sensory signal is central to perception and cognition and hence is a critical task for future artificial intelligence systems. In this p…
In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory
Geethan Karunaratne, Michael Hersche, Jovin Langenegger +15
Continually learning new classes from a few training examples without forgetting previous old classes demands a flexible architecture with an inevitably growing portion of storage,…
Scale up your In-Memory Accelerator: Leveraging Wireless-on-Chip Communication for AIMC-based CNN Inference
Nazareno Bruschi, Giuseppe Tagliavini, Francesco Conti +7
Analog In-Memory Computing (AIMC) is emerging as a disruptive paradigm for heterogeneous computing, potentially delivering orders of magnitude better peak performance and efficienc…
Wireless On-Chip Communications for Scalable In-memory Hyperdimensional Computing
Robert Guirado, Abbas Rahimi, Geethan Karunaratne +3
Hyperdimensional computing (HDC) is an emerging computing paradigm that represents, manipulates, and communicates data using very long random vectors (aka hypervectors). Among diff…
Constrained Few-shot Class-incremental Learning
Michael Hersche, Geethan Karunaratne, Giovanni Cherubini +3
Continually learning new classes from fresh data without forgetting previous knowledge of old classes is a very challenging research problem. Moreover, it is imperative that such l…
Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks
Denis Kleyko, Geethan Karunaratne, Jan M. Rabaey +2
Memory-augmented neural networks enhance a neural network with an external key-value memory whose complexity is typically dominated by the number of support vectors in the key memo…
A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networks
Angelo Garofalo, Gianmarco Ottavi, Francesco Conti +4
Deployment of modern TinyML tasks on small battery-constrained IoT devices requires high computational energy efficiency. Analog In-Memory Computing (IMC) using non-volatile memory…
End-to-end 100-TOPS/W Inference With Analog In-Memory Computing: Are We There Yet?
Gianmarco Ottavi, Geethan Karunaratne, Francesco Conti +3
In-Memory Acceleration (IMA) promises major efficiency improvements in deep neural network (DNN) inference, but challenges remain in the integration of IMA within a digital system.…
Energy Efficient In-memory Hyperdimensional Encoding for Spatio-temporal Signal Processing
Geethan Karunaratne, Manuel Le Gallo, Michael Hersche +4
The emerging brain-inspired computing paradigm known as hyperdimensional computing (HDC) has been proven to provide a lightweight learning framework for various cognitive tasks com…
Graphene-based Wireless Agile Interconnects for Massive Heterogeneous Multi-chip Processors
Sergi Abadal, Robert Guirado, Hamidreza Taghvaee +18
The main design principles in computer architecture have recently shifted from a monolithic scaling-driven approach to the development of heterogeneous architectures that tightly c…
Robust High-dimensional Memory-augmented Neural Networks
Geethan Karunaratne, Manuel Schmuck, Manuel Le Gallo +4
Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapti…
ChewBaccaNN: A Flexible 223 TOPS/W BNN Accelerator
Renzo Andri, Geethan Karunaratne, Lukas Cavigelli +1
Binary Neural Networks enable smart IoT devices, as they significantly reduce the required memory footprint and computational complexity while retaining a high network performance…
ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning
Vinay Joshi, Geethan Karunaratne, Manuel Le Gallo +5
Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits…
Mixed-precision deep learning based on computational memory
S. R. Nandakumar, Manuel Le Gallo, Christophe Piveteau +11
Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition…
In-memory hyperdimensional computing
Geethan Karunaratne, Manuel Le Gallo, Giovanni Cherubini +3
Hyperdimensional computing (HDC) is an emerging computational framework that takes inspiration from attributes of neuronal circuits such as hyperdimensionality, fully distributed h…
XNORBIN: A 95 TOp/s/W Hardware Accelerator for Binary Convolutional Neural Networks
Andrawes Al Bahou, Geethan Karunaratne, Renzo Andri +2
Deploying state-of-the-art CNNs requires power-hungry processors and off-chip memory. This precludes the implementation of CNNs in low-power embedded systems. Recent research shows…