HIERARCHICALTEMPORAL MEMORY

HIERARCHICALTEMPORAL MEMORY

HIERARCHICALTEMPORAL MEMORY ON
THE AUTOMATA PROCESSOR

Work related to accelerating HTM
Previous work has introduced an accelerator
Architectures for HTM. Sebastian Billaudelle
And Subutai Ahmad carried HTM basic functions
In a spyware
Integrated and verified circuit
Met HTM basiques.2 properties Abdullah Zyarah
An impressive acceleration demonstrated in champprogrammable
The application of the gate array (FPGA)
Using small (100 columns, 300 cells)
HTM against basic Matlab.3 Xi Zhou
And Yaoyao Luo evaluated the HTM algorithms
In a systolic matrix of multilevel architecture,
Showing a quasi-linear acceleration core, with 64
HTM in a single application in the vision ordinateur.4
Finally, Mandar Deshpande built a
FPGA main implementation
Component Analysis Inspired by HTM.5
The hierarchical temporal memory
In fact, HTM performs learning, inference,
And the prediction of a continuous flow of
Incoming tickets. As shown in Figure 1, HTM
The core comprises two main modules, space
Pooler and the temporary memory, preceded
For a scarce distributed representation (CSD)
Encoder input, then followed by a CSD
Decoder output module. The encoder
Translated inputs truly valuable SDR
They are passed through the spatial and temporal pooler
Memory modules, where they cause the
Changes in cells and predictions of what inputs
It will be seen later. The state of the cells is then
Transmits to a decoder module;
Translated these dispersed cell activations
In real results, to interact with
Other systèmes.6
Distributed distributed representations
SDRs are neo-inspired data format
For HTM. SDRs are large binary vectors
(For example, 2048 bits) with only a few bits of
(Usually about 2 percent) SET (“1”), in
That encodes each bit semantic meaning.
DEG has several valuable properties,
Including the robustness and
To quickly determine the semantic similarity bit
Comparison, two of which are operated
The HTM-AP model.
The spatial module of the temporary memory and pooler
HTM to use a sequence of discrete units
Coded SDG format is entered
The space pooler. Each pattern entered
Allows a set of columns and scattered cells
In these columns. Cell activations
The temporal memory in turn predicted that the cells
Allows the following synaptic connections
Between the exits and the cells entered. Synaptic
The connections to the predicted cells
In the contribution,
Those who were not weakened. Forming
Predictions about what is going to happen next and
Adjust the connections as activations,
HTM learns and performs the inference
at the same time. In any case,
The predicted state of all cells is an HTM
Sortie.8
Qualitatively, each column corresponds to activation
In recognition of a semantic
A meaning and collection of active columns
Recognize something described by the union
From these meanings. The activation of the cells within
These columns encode the context between
The sequences of things. For example, if an HTM
It is formed with the sequence ABCD and
XYCD, and then presented with AB or
XY, most of the same columns you could predict
For C in both cases, but the cells
In these columns would be different,
Reflecting the difference in context

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