Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Shafik, Rishad"'
Autor:
Ghazal, Omar, Lan, Tian, Ojukwu, Shalman, Krishnamurthy, Komal, Yakovlev, Alex, Shafik, Rishad
The modern implementation of machine learning architectures faces significant challenges due to frequent data transfer between memory and processing units. In-memory computing, primarily through memristor-based analog computing, offers a promising so
Externí odkaz:
http://arxiv.org/abs/2408.09456
The Tsetlin Machine (TM) has gained significant attention in Machine Learning (ML). By employing logical fundamentals, it facilitates pattern learning and representation, offering an alternative approach for developing comprehensible Artificial Intel
Externí odkaz:
http://arxiv.org/abs/2407.09162
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 for predicting NMIBC recurrence rely on scoring systems that often ov
Externí odkaz:
http://arxiv.org/abs/2403.10586
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 accelerator systems. However, the design effort for training and transla
Externí odkaz:
http://arxiv.org/abs/2403.10538
Autor:
Bhattarai, Bimal, Granmo, Ole-Christoffer, Jiao, Lei, Andersen, Per-Arne, Tunheim, Svein Anders, Shafik, Rishad, Yakovlev, Alex
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 absorbing Include state, making the learning scheme absorbing instead
Externí odkaz:
http://arxiv.org/abs/2310.11481
Autor:
Prescott, Samuel, Wheeldon, Adrian, Shafik, Rishad, Rahman, Tousif, Yakovlev, Alex, Granmo, Ole-Christoffer
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 be small compared to the data-points presented to the system during no
Externí odkaz:
http://arxiv.org/abs/2306.01027
Autor:
Ghazal, Omar, Singh, Simranjeet, Rahman, Tousif, Yu, Shengqi, Zheng, Yujin, Balsamo, Domenico, Patkar, Sachin, Merchant, Farhad, Xia, Fei, Yakovlev, Alex, Shafik, Rishad
In-memory computing for Machine Learning (ML) applications remedies the von Neumann bottlenecks by organizing computation to exploit parallelism and locality. Non-volatile memory devices such as Resistive RAM (ReRAM) offer integrated switching and st
Externí odkaz:
http://arxiv.org/abs/2305.12914
Energy efficiency is a crucial requirement for enabling powerful artificial intelligence applications at the microedge. Hardware acceleration with frugal architectural allocation is an effective method for reducing energy. Many emerging applications
Externí odkaz:
http://arxiv.org/abs/2305.11928
Autor:
Singh, Simranjeet, Ghazal, Omar, Jha, Chandan Kumar, Rana, Vikas, Drechsler, Rolf, Shafik, Rishad, Yakovlev, Alex, Patkar, Sachin, Merchant, Farhad
Data movement costs constitute a significant bottleneck in modern machine learning (ML) systems. When combined with the computational complexity of algorithms, such as neural networks, designing hardware accelerators with low energy footprint remains
Externí odkaz:
http://arxiv.org/abs/2304.13552
We present a hardware design for the learning datapath of the Tsetlin machine algorithm, along with a latency analysis of the inference datapath. In order to generate a low energy hardware which is suitable for pervasive artificial intelligence appli
Externí odkaz:
http://arxiv.org/abs/2109.00846