10 citations · 22 across the 27 of their papers we have counts for
31 papers
Event-Driven Digital-Time-Domain Inference Architectures for Tsetlin Machines
Tian Lan, Rishad Shafik, Alex Yakovlev
Machine learning fits model parameters to approximate input-output mappings, predicting unknown samples. However, these models often require extensive arithmetic computations durin…
Fast and Compact Tsetlin Machine Inference on CPUs Using Instruction-Level Optimization
Yefan Zeng, Shengyu Duan, Rishad Shafik +1
The Tsetlin Machine (TM) offers high-speed inference on resource-constrained devices such as CPUs. Its logic-driven operations naturally lend themselves to parallel execution on mo…
A Tsetlin Machine Image Classification Accelerator on a Flexible Substrate
Yushu Qin, Marcos L. L. Sartori, Shengyu Duan +3
This paper introduces the first implementation of digital Tsetlin Machines (TMs) on flexible integrated circuit (FlexIC) using Pragmatic's 600nm IGZO-based FlexIC technology. TMs,…
A Methodology for Transparent Logic-Based Classification Using a Multi-Task Convolutional Tsetlin Machine
Mayur Kishor Shende, Ole-Christoffer Granmo, Runar Helin +2
The Tsetlin Machine (TM) is a novel machine learning paradigm that employs finite-state automata for learning and utilizes propositional logic to represent patterns. Due to its sim…
Uncertainty Quantification in the Tsetlin Machine
Runar Helin, Ole-Christoffer Granmo, Mayur Kishor Shende +5
Data modeling using Tsetlin machines (TMs) is all about building logical rules from the data features. The decisions of the model are based on a combination of these logical rules.…
Omni TM-AE: A Scalable and Interpretable Embedding Model Using the Full Tsetlin Machine State Space
Ahmed K. Kadhim, Lei Jiao, Rishad Shafik +1
The increasing complexity of large-scale language models has amplified concerns regarding their interpretability and reusability. While traditional embedding models like Word2Vec a…
Efficient FPGA Implementation of Time-Domain Popcount for Low-Complexity Machine Learning
Shengyu Duan, Marcos L. L. Sartori, Rishad Shafik +2
Population count (popcount) is a crucial operation for many low-complexity machine learning (ML) algorithms, including Tsetlin Machine (TM)-a promising new ML method, particularly…
Attention-enabled Explainable AI for Bladder Cancer Recurrence Prediction
Saram Abbas, Naeem Soomro, Rishad Shafik +2
Non-muscle-invasive bladder cancer (NMIBC) is a relentless challenge in oncology, with recurrence rates soaring as high as 70-80%. Each recurrence triggers a cascade of invasive pr…
Dynamic Tsetlin Machine Accelerators for On-Chip Training at the Edge using FPGAs
Gang Mao, Tousif Rahman, Sidharth Maheshwari +4
The increased demand for data privacy and security in machine learning (ML) applications has put impetus on effective edge training on Internet-of-Things (IoT) nodes. Edge training…
Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAs
Tousif Rahman, Gang Mao, Bob Pattison +5
Embedded Field-Programmable Gate Arrays (eFPGAs) allow for the design of hardware accelerators of edge Machine Learning (ML) applications at a lower power budget compared with trad…
ETHEREAL: Energy-efficient and High-throughput Inference using Compressed Tsetlin Machine
Shengyu Duan, Rishad Shafik, Alex Yakovlev
The Tsetlin Machine (TM) is a novel alternative to deep neural networks (DNNs). Unlike DNNs, which rely on multi-path arithmetic operations, a TM learns propositional logic pattern…
An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Accelerator
Svein Anders Tunheim, Yujin Zheng, Lei Jiao +3
We present an all-digital programmable machine learning accelerator chip for image classification, underpinning on the Tsetlin machine (TM) principles. The TM is an emerging machin…
Scalable Multi-phase Word Embedding Using Conjunctive Propositional Clauses
Ahmed K. Kadhim, Lei Jiao, Rishad Shafik +2
The Tsetlin Machine (TM) architecture has recently demonstrated effectiveness in Machine Learning (ML), particularly within Natural Language Processing (NLP). It has been utilized…
Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings
Ahmed K. Kadhim, Lei Jiao, Rishad Shafik +1
In recent years, text generation tools utilizing Artificial Intelligence (AI) have occasionally been misused across various domains, such as generating student reports or creative…
IMPACT:InMemory ComPuting Architecture Based on Y-FlAsh Technology for Coalesced Tsetlin Machine Inference
Omar Ghazal, Wei Wang, Shahar Kvatinsky +3
The increasing demand for processing large volumes of data for machine learning models has pushed data bandwidth requirements beyond the capability of traditional von Neumann archi…
In-Memory Learning Automata Architecture using Y-Flash Cell
Omar Ghazal, Tian Lan, Shalman Ojukwu +3
The modern implementation of machine learning architectures faces significant challenges due to frequent data transfer between memory and processing units. In-memory computing, pri…
Exploring State Space and Reasoning by Elimination in Tsetlin Machines
Ahmed K. Kadhim, Ole-Christoffer Granmo, Lei Jiao +1
The Tsetlin Machine (TM) has gained significant attention in Machine Learning (ML). By employing logical fundamentals, it facilitates pattern learning and representation, offering…
MATADOR: Automated System-on-Chip Tsetlin Machine Design Generation for Edge Applications
Tousif Rahman, Gang Mao, Sidharth Maheshwari +2
System-on-Chip Field-Programmable Gate Arrays (SoC-FPGAs) offer significant throughput gains for machine learning (ML) edge inference applications via the design of co-processor ac…
Reviewing AI's Role in Non-Muscle-Invasive Bladder Cancer Recurrence Prediction
Saram Abbas, Rishad Shafik, Naeem Soomro +2
Notorious for its 70-80% recurrence rate, Non-muscle-invasive Bladder Cancer (NMIBC) imposes a significant human burden and is one of the costliest cancers to manage. Current tools…
Contracting Tsetlin Machine with Absorbing Automata
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao +4
In this paper, we introduce a sparse Tsetlin Machine (TM) with absorbing Tsetlin Automata (TA) states. In brief, the TA of each clause literal has both an absorbing Exclude- and an…
An FPGA Architecture for Online Learning using the Tsetlin Machine
Samuel Prescott, Adrian Wheeldon, Rishad Shafik +3
There is a need for machine learning models to evolve in unsupervised circumstances. New classifications may be introduced, unexpected faults may occur, or the initial dataset may…
IMBUE: In-Memory Boolean-to-CUrrent Inference ArchitecturE for Tsetlin Machines
Omar Ghazal, Simranjeet Singh, Tousif Rahman +8
In-memory computing for Machine Learning (ML) applications remedies the von Neumann bottlenecks by organizing computation to exploit parallelism and locality. Non-volatile memory d…
Energy-frugal and Interpretable AI Hardware Design using Learning Automata
Rishad Shafik, Tousif Rahman, Adrian Wheeldon +2
Energy efficiency is a crucial requirement for enabling powerful artificial intelligence applications at the microedge. Hardware acceleration with frugal architectural allocation i…
Finite State Automata Design using 1T1R ReRAM Crossbar
Simranjeet Singh, Omar Ghazal, Chandan Kumar Jha +6
Data movement costs constitute a significant bottleneck in modern machine learning (ML) systems. When combined with the computational complexity of algorithms, such as neural netwo…