A brain-like computing chip and computing equipment

A computing chip and brain-like technology, applied in the field of brain-like computing chips and computing equipment, can solve problems such as low computing efficiency, insufficient programmability, and inability to support models

Active Publication Date: 2019-06-18
LYNXI TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional brain-like chips (neuromorphic chips) will only consider 1-2 of the models and implement them in a pure ASIC manner. Although most of them have high execution efficiency and low power consumption, they are not programmable enough to support more models
Although a programmable neuromorphic chip with many processor cores is used in traditional chips, each core is a general-purpose ARM processor, but because it does not have an additional acceleration module for multiplying and adding, the calculation efficiency is low

Method used

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  • A brain-like computing chip and computing equipment
  • A brain-like computing chip and computing equipment
  • A brain-like computing chip and computing equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0054] Embodiment one, Leaky Integrate and Fire (LIF) model

[0055] 1. Update neuron membrane potential value: update the membrane potential value V of LIF pulsed neurons at time t 1 (t), the membrane potential value V of the neuron at time t 1 (t) can be expressed as: axon input X(t) and synaptic weight W 1 The dot product of , and the membrane potential value V maintained by the neuron at the last moment 1rst (t) The sum of the values ​​after the effect of the membrane potential decay coefficient α, as shown in the following formula:

[0056] V 1 (t)=X(t)·W 1 +αV 1rst (t-1) formula (1)

[0057] 2. Neuron output judgment: the output of the LIF pulse neuron is the presence or absence of a pulse represented by 0 or 1, and the output pulse y 1 (t) The neuronal membrane potential V at time t 1 (t) After adding the bias term b (also known as the Changzhi attenuation term), it is related to the neuron threshold V th For comparison, when the biased membrane potential value...

Embodiment 2

[0080] Embodiment 2 Izhikevich Model model

[0081] The Izhikevich Model uses a differential equation to represent the update of the neuron membrane potential, as shown in the following formula, where v represents the membrane potential of the Izhikevich neuron; u is the recovery variable, which is used to describe the Na in the biological neuron + 、K + I represents the input stimulus received by the neuron, similar to the first embodiment of the invention, the input stimulus is obtained by the product of the axon input X and the synaptic weight W.

[0082] Formula (4) shows that the change of neuron membrane potential v over time is determined by itself, the current recovery variable u and the input stimulus I, and formula (5) shows that the change of recovery variable u over time is determined by itself and the current The neuron membrane potential v is jointly determined, and the formula (6) describes the output judgment of the neuron and the reset update logic of the neur...

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PUM

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Abstract

The invention provides a brain-like computing chip and computing equipment, the brain-like computing chip comprises a many-core system composed of one or more functional cores, and the functional cores perform data transmission through a network-on-chip; each functional core comprises at least one neuron processor used for calculating a plurality of neuron models; and at least one coprocessor which is coupled with the neuron processor and is used for executing an integral operation and / or a multiply-add operation; Wherein the neuron processor can call the coprocessor to execute multiplicationand addition type operation. The brain-like computing chip provided by the invention is used for efficiently realizing various neural morphology algorithms, especially for computing characteristics ofan SNN-rich model, and has synaptic computing with high computing density and cell body computing with high flexibility requirements.

Description

technical field [0001] The invention relates to the field of chip technology, in particular to a brain-inspired computing chip and computing equipment. Background technique [0002] At present, there are many kinds of artificial neuron models according to the degree of sophistication and emphases of simulating biological neurons. Such as the most typical Integrate and fire (IF) model, Leaky integrate-and-fire (LIF) model and Izhikevich model. Traditional brain-like chips (neuromorphic chips) will only consider 1-2 of the models and implement them in a pure ASIC manner. Although most of them have high execution efficiency and low power consumption, they are not programmable enough to support more models . Although a programmable neuromorphic chip with many processor cores is used in traditional chips, each core is a general-purpose ARM processor, but because it does not have an additional acceleration module for multiplying and adding, the calculation efficiency is low. C...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/30G06N3/06G06N3/063
CPCG06N3/063G06F9/30G06N3/049G06F7/5443
Inventor 吴臻志吴冠睿
Owner LYNXI TECH CO LTD
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