Zobrazeno 1 - 10
of 4 549
pro vyhledávání: '"A A, Patkar"'
Resistive random-access memory (RRAM) is gaining popularity due to its ability to offer computing within the memory and its non-volatile nature. The unique properties of RRAM, such as binary switching, multi-state switching, and device variations, ca
Externí odkaz:
http://arxiv.org/abs/2407.03843
Autor:
Singh, Simranjeet, Bende, Ankit, Jha, Chandan Kumar, Rana, Vikas, Drechsler, Rolf, Patkar, Sachin, Merchant, Farhad
In-memory computing (IMC) has gained significant attention recently as it attempts to reduce the impact of memory bottlenecks. Numerous schemes for digital IMC are presented in the literature, focusing on logic operations. Often, an application's des
Externí odkaz:
http://arxiv.org/abs/2407.02921
Autor:
Datar, Mandar, Hegde, Dhruva S., Prasad, Vendra Durga, Prajapati, Manish, Manikanta, Neralla, Gupta, Devansh, Pavanija, Janampalli, Pare, Pratyush, Akash, Gupta, Shivam, Patkar, Sachin B.
We have designed a Python-based Domain Specific Language (DSL) for modeling synchronous digital circuits. In this DSL, hardware is modeled as a collection of transactions -- running in series, parallel, and loops. When the model is executed by a Pyth
Externí odkaz:
http://arxiv.org/abs/2406.09208
Autor:
Bende, Ankit, Singh, Simranjeet, Jha, Chandan Kumar, Kempen, Tim, Cüppers, Felix, Bengel, Christopher, Zambanini, Andre, Nielinger, Dennis, Patkar, Sachin, Drechsler, Rolf, Waser, Rainer, Merchant, Farhad, Rana, Vikas
Memristor-aided logic (MAGIC) design style holds a high promise for realizing digital logic-in-memory functionality. The ability to implement a specific gate in a MAGIC design style hinges on the SET-to-RESET threshold ratio. The TaOx memristive devi
Externí odkaz:
http://arxiv.org/abs/2310.10460
Autor:
Singh, Simranjeet, Jha, Chandan Kumar, Bende, Ankit, Rana, Vikas, Patkar, Sachin, Drechsler, Rolf, Merchant, Farhad
Existing logic-in-memory (LiM) research is limited to generating mappings and micro-operations. In this paper, we present~\emph{MemSPICE}, a novel framework that addresses this gap by automatically generating both the netlist and testbench needed to
Externí odkaz:
http://arxiv.org/abs/2309.04868
Autor:
Sahil Sahni, Binbin Wang, Di Wu, Saugato Rahman Dhruba, Matthew Nagy, Sushant Patkar, Ingrid Ferreira, Chi-Ping Day, Kun Wang, Eytan Ruppin
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-15 (2024)
Abstract Immune checkpoint blockade (ICB) is a promising cancer therapy; however, resistance frequently develops. To explore ICB resistance mechanisms, we develop Immunotherapy Resistance cell-cell Interaction Scanner (IRIS), a machine learning model
Externí odkaz:
https://doaj.org/article/dedbbace84e14f83bf231cfe1e8f819c
Publikováno v:
National Board of Examinations Journal of Medical Sciences, Vol Volume 2, Iss Issue 8, Pp 832-836 (2024)
Background: Germ cell tumours (GCT) are tumours arising from primordial germ cells. Gonadal and extragonadal GCT are infrequent in childhood occurring at a rate of 2.4 cases per million children accounting for 2-3 % of paediatric malignancies with mo
Externí odkaz:
https://doaj.org/article/0c174298143640a48b9df1da9632766d
Autor:
Singh, Simranjeet, Jha, Chandan Kumar, Bende, Ankit, Thangkhiew, Phrangboklang Lyngton, Rana, Vikas, Patkar, Sachin, Drechsler, Rolf, Merchant, Farhad
Memristor-based logic-in-memory (LiM) has become popular as a means to overcome the von Neumann bottleneck in traditional data-intensive computing. Recently, the memristor-aided logic (MAGIC) design style has gained immense traction for LiM due to it
Externí odkaz:
http://arxiv.org/abs/2307.03669
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
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