Neuromorphic scaling advantages for energy-efficient random walk computations

Smith, J. Darby; Hill, Aaron Jamison; Reeder, Leah; Franke, Brian Claude; Lehoucq, Richard B.; Parekh, Ojas; Severa, William; Aimone, James B.

doi:10.1038/s41928-021-00705-7

Cited by 25 publications

(13 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, our data showed that FDX1 expression level was positively associated with cell proliferative cell pathway such as RTKs, RAS/MAPK and PI3k/AKT pathway in LIHC and THCA, while there was no significant correlation in other tumor types. Copper was reported to be a dynamic signaling metal to regulate cell behavior, for example, it was involved in mitogen-activated protein kinase 1 (MEK1) and MEK2 pathway ( Smith et al, 2022 ). The role and mechanisms of FDX1 in cell signaling needed to explored in further studies.…”

Section: Discussionmentioning

confidence: 99%

Characterization of the functional effects of ferredoxin 1 as a cuproptosis biomarker in cancer

Dai

Liu

et al. 2022

Front. Genet.

View full text Add to dashboard Cite

Background: Cuproptosis is a recently discovered form of programmed cell death. Ferredoxin 1 (FDX1) is a key gene that mediates this process. However, the role of FDX1 in human tumors is not clear.Methods: We comprehensively analyzed the differential expression and genetic alterations of FDX1 using multiomics data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. Subsequently, we explored the association between FDX1 and tumor parameters such as genomic instability, RNA methylation modifications, immune infiltration and pathway activity. In addition, we performed functional enrichment analysis and assessed the sensitivity potential of FDX1-related drugs. Finally, we experimentally verified the functional effects of FDX1.Results: The analysis revealed differential expression of FDX1 in a variety of tumors. By analyzing the association of FDX1 expression with genomic instability, immune cell infiltration, signaling pathway etc. We explored the role of FDX1 in regulating cell activity. Also, we evaluated the function of FDX1 in biologic process and drug sensitivity. Our experimental results demonstrated that FDX1 exerts its antitumor effects through cuproptosis in liver hepatocellular carcinoma and non-small cell lung cancer cell lines.Conclusion: Our study reveals the functional effects of FDX1 in tumors and deepens the understanding of the effects of FDX1. We validated the inhibitory effect of FDX1 in copper induced cell-death, confirming the role of FDX1 as a cuproptosis biomarker.

show abstract

Section: Discussionmentioning

confidence: 99%

Characterization of the functional effects of ferredoxin 1 as a cuproptosis biomarker in cancer

Dai

Liu

et al. 2022

Front. Genet.

View full text Add to dashboard Cite

show abstract

“…Neuromorphic technologies are still new and it is reasonable to ask whether or not the stochastic process sampling accomplished by the graph embedded DTMC provides statistically reasonable data. In [84], it is shown that Loihi approximates a solution to a heat equation on a sphere as if it were sampling the underlying Markov chain with seven-bit limited transition probabilities. This suggests that in addition to approximation and application specific error accrued when constructing a DTMC from a stochastic process, there may be additional hardware and algorithmic specific error introduced.…”

Section: Neuromorphic Advantages and Remaining Challenges For Random ...mentioning

confidence: 99%

“…Then, the discrete states of the chain can be chosen by discretizing space. A transition matrix can be created by integrating the pdfs over these space bins (see [84]). In general, (10) translates nicely to such an interpretation.…”

Section: Markov Chains and Pide Solutionsmentioning

confidence: 99%

A review of non-cognitive applications for neuromorphic computing

Aimone

Date

Guerra

et al. 2022

Neuromorph. Comput. Eng.

Self Cite

View full text Add to dashboard Cite

Though neuromorphic computers have typically targeted applications in machine learning and neuroscience (``cognitive'' applications), they have many computational characteristics that are attractive for a wide variety of computational problems. In this work, we review the current state-of-the-art for non-cognitive applications on neuromorphic computers, including simple computational kernels for composition, graph algorithms, constrained optimization, and signal processing. We discuss the advantages of using neuromorphic computers for these different applications, as well as the challenges that still remain. The ultimate goal of this work is to bring awareness to this class of problems for neuromorphic systems to the broader community, particularly to encourage further work in this area and to make sure that these applications are considered in the design of future neuromorphic systems.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Both of the applications that we consider have been studied in neuromorphic circuits previously [29][30][31][32]. In [32] the authors identify neuromorphic random walk algorithms as having advantages, in the sense that they are more energy-efficient and scalable, over their equivalents on standard CPU and GPU architectures. In [32], a more complicated, modular circuit architecture is used that keeps aspects of the random walk, while at the same time accumulating population amplitudes in a single system.…”

Section: Introductionmentioning

confidence: 99%

Binary operations on neuromorphic hardware with application to linear algebraic operations and stochastic equations

Iaroshenko

Sornborger

Arana

2023

Neuromorph. Comput. Eng.

View full text Add to dashboard Cite

Non-von Neumann computational hardware, based on neuron-inspired, non-linear elements connected via linear, weighted synapses -- so-called neuromorphic systems -- is a viable computational substrate. Since neuromorphic systems have been shown to use less power than CPUs for many applications, they are of potential use in autonomous systems such as robots, drones, and satellites, for which power resources are at a premium. The power used by neuromorphic systems is approximately proportional to the number of spiking events produced by neurons on-chip. However, typical information encoding on these chips is in the form of firing rates that unarily encode information. That is, the number of spikes generated by a neuron is meant to be proportional to an encoded value used in a computation or algorithm. Unary encoding is less efficient (produces more spikes) than binary encoding. For this reason, here we present neuromorphic computational mechanisms for implementing binary two's complement operations. We use the mechanisms to construct a neuromorphic, binary matrix multiplication algorithm that may be used as a primitive for linear differential equation integration, deep networks, and other standard calculations. We also construct a random walk circuit and apply it in Brownian motion simulations. We study how both algorithms scale in circuit size and iteration time.

show abstract

Neuromorphic scaling advantages for energy-efficient random walk computations

Cited by 25 publications

References 41 publications

Characterization of the functional effects of ferredoxin 1 as a cuproptosis biomarker in cancer

Characterization of the functional effects of ferredoxin 1 as a cuproptosis biomarker in cancer

A review of non-cognitive applications for neuromorphic computing

Binary operations on neuromorphic hardware with application to linear algebraic operations and stochastic equations

Contact Info

Product

Resources

About