Abstract
A neuro-inspired computing paradigm beyond the von Neumann architecture is emerging and it generally takes advantage of massive parallelism and is aimed at complex tasks that involve intelligence and learning. The cross-point array architecture with synaptic devices has been proposed for on-chip implementation of the weighted sum and weight update in the learning algorithms. In this work, forming-free, silicon-process-compatible Ta/TaOx/TiO2/Ti synaptic devices are fabricated, in which >200 levels of conductance states could be continuously tuned by identical programming pulses. In order to demonstrate the advantages of parallelism of the cross-point array architecture, a novel fully parallel write scheme is designed and experimentally demonstrated in a small-scale crossbar array to accelerate the weight update in the training process, at a speed that is independent of the array size. Compared to the conventional row-by-row write scheme, it achieves >30× speed-up and >30× improvement in energy efficiency as projected in a large-scale array. If realistic synaptic device characteristics such as device variations are taken into an array-level simulation, the proposed array architecture is able to achieve ∼95% recognition accuracy of MNIST handwritten digits, which is close to the accuracy achieved by software using the ideal sparse coding algorithm.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 455204 |
| Number of pages | 1 |
| Journal | Nanotechnology |
| Volume | 26 |
| Issue number | 45 |
| State | Published - Nov 13 2015 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Medicine(all)
Cite this
Fully parallel write/read in resistive synaptic array for accelerating on-chip learning. / Gao, Ligang; Wang, I. Ting; Chen, Pai Yu; Vrudhula, Sarma; Seo, Jae sun; Cao, Yu; Hou, Tuo Hung; Yu, Shimeng.
In: Nanotechnology, Vol. 26, No. 45, 13.11.2015, p. 455204.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Fully parallel write/read in resistive synaptic array for accelerating on-chip learning
AU - Gao, Ligang
AU - Wang, I. Ting
AU - Chen, Pai Yu
AU - Vrudhula, Sarma
AU - Seo, Jae sun
AU - Cao, Yu
AU - Hou, Tuo Hung
AU - Yu, Shimeng
PY - 2015/11/13
Y1 - 2015/11/13
N2 - A neuro-inspired computing paradigm beyond the von Neumann architecture is emerging and it generally takes advantage of massive parallelism and is aimed at complex tasks that involve intelligence and learning. The cross-point array architecture with synaptic devices has been proposed for on-chip implementation of the weighted sum and weight update in the learning algorithms. In this work, forming-free, silicon-process-compatible Ta/TaOx/TiO2/Ti synaptic devices are fabricated, in which >200 levels of conductance states could be continuously tuned by identical programming pulses. In order to demonstrate the advantages of parallelism of the cross-point array architecture, a novel fully parallel write scheme is designed and experimentally demonstrated in a small-scale crossbar array to accelerate the weight update in the training process, at a speed that is independent of the array size. Compared to the conventional row-by-row write scheme, it achieves >30× speed-up and >30× improvement in energy efficiency as projected in a large-scale array. If realistic synaptic device characteristics such as device variations are taken into an array-level simulation, the proposed array architecture is able to achieve ∼95% recognition accuracy of MNIST handwritten digits, which is close to the accuracy achieved by software using the ideal sparse coding algorithm.
AB - A neuro-inspired computing paradigm beyond the von Neumann architecture is emerging and it generally takes advantage of massive parallelism and is aimed at complex tasks that involve intelligence and learning. The cross-point array architecture with synaptic devices has been proposed for on-chip implementation of the weighted sum and weight update in the learning algorithms. In this work, forming-free, silicon-process-compatible Ta/TaOx/TiO2/Ti synaptic devices are fabricated, in which >200 levels of conductance states could be continuously tuned by identical programming pulses. In order to demonstrate the advantages of parallelism of the cross-point array architecture, a novel fully parallel write scheme is designed and experimentally demonstrated in a small-scale crossbar array to accelerate the weight update in the training process, at a speed that is independent of the array size. Compared to the conventional row-by-row write scheme, it achieves >30× speed-up and >30× improvement in energy efficiency as projected in a large-scale array. If realistic synaptic device characteristics such as device variations are taken into an array-level simulation, the proposed array architecture is able to achieve ∼95% recognition accuracy of MNIST handwritten digits, which is close to the accuracy achieved by software using the ideal sparse coding algorithm.
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M3 - Article
C2 - 26491032
VL - 26
SP - 455204
JO - Nanotechnology
JF - Nanotechnology
SN - 0957-4484
IS - 45
ER -