SYSTEMS AND METHODS FOR CONTINUOUS & REAL-TIME AI ADAPTIVE SENSE LEARNING
Aspects of the present disclosure are presented for an autonomous adaptive AI self-learning, training and inferencing system and method that would provide extremely cost effective and energy efficient broad based AI solutions/applications that are personalized/customizable. In some embodiments, a proposed component is an intelligent sense neuro memory cell unit (ISN-MCU). The ISN-MCU acts as the basic building block for AI adaptive learning. Each ISN-MCU is capable of receiving input from the surrounding environment and then learning or making an inference about the received data. With enough time or many more ISN-MCUs in combination, the AI system may be capable of learning for what it was programmed for in real time and in a memory and time efficient manner.
1 . An intelligent sense neuro memory cell unit (ISN-MCU) apparatus, comprising:
one or more sense elements configured to receive sensory data from an external environment;
at least one sampler module configured to continuously sample the one or more sense elements to ingest the sensory data;
a neural cell communicatively coupled to the at least one sampler and configured to adaptively learn using the received sensory data;
a memory cell communicatively coupled to the neural cell and configured to store learned inferences from the neural cell based on what the neural cell learns from the received sensory data; and
a digital input/output interface communicatively coupled to the memory cell and configured to interface the ISN-MCU to a digital domain.
2 . The ISN-MCU apparatus of claim 1 , wherein the memory cell is a multi-bit memory cell comprising:
an input memory cell configured to store input sampler data received from the at least one sampler module;
a weight/adaptivity quotient memory cell configured to store weight/adaptivity quotient data; and
an output memory cell configured to store an inference.
3 . The ISN-MCU apparatus of claim 1 , configured to provide visual and auditory inferences;
wherein the one or more sense elements comprise audio and visual sensors.
4 . The ISN-MCU apparatus of claim 1 , configured to sense distance and imagery data, wherein the one or more sense elements comprise lidar sensors.
5 . The ISN-MCU apparatus of claim 1 , configured to identify chemicals, wherein the one or more sense elements comprise chemical sensors configured to convert chemical signatures into analyzable data.
6 . The ISN-MCU apparatus of claim 5 , wherein the chemical sensors comprise smell-based sensors.
7 . The ISN-MCU apparatus of claim 5 , wherein the chemical sensors comprise taste-based sensors.
8 . The ISN-MCU apparatus of claim 1 , configured to sense biological data, wherein the one or more sense elements comprise a biosensor.
9 . The ISN-MCU apparatus of claim 1 , configured to perform artificial intelligence (AI) model inference operations while simultaneously performing self-learning operations using backpropagation techniques.
10 . An adaptive intelligent processing logic unit (ADI-PLU) apparatus, comprising:
a control channel configured to receive a command and transmit said command to multiple entities in parallel;
a plurality of ISN-MCUs communicatively coupled in parallel to the control bus, each of the plurality of ISN-MCUs comprising:
one or more sense elements configured to receive sensory data from an external environment;
at least one sampler module configured to continuously sample the one or more sense elements to ingest the sensory data;
a neural cell communicatively coupled to the at least one sampler and configured to adaptively learn using the received sensory data;
a memory cell communicatively coupled to the neural cell and configured to store learned inferences from the neural cell based on what the neural cell learns from the received sensory data; and
a digital input/output interface communicatively coupled to the memory cell and
configured to interface the ISN-MCU to a digital domain;
wherein each of the ISN-MCUs are configured to perform a learning operation in parallel with the other ISN-MCUs to learn from sensory data in an external environment based on receiving the command from the control channel.
11 . An apparatus comprising:
a plurality of ADI-PLUs of claim 9 , arranged in a hierarchical manner; and
a hierarchical non-blocking interconnect module configured to interconnect the plurality of ADI-PLUs.
12 . The apparatus of claim 11 , further comprising a lookup and forwarding table configured to provide automatic forwarding of data between the plurality of ADI-PLUs and their respective ISN-MCUs.
13 . The ADI-PLU apparatus of claim 10 , wherein the memory cell is a multi-bit memory cell comprising:
an input memory cell configured to store input sampler data received from the at least one sampler module;
a weight/adaptivity quotient memory cell configured to store weight/adaptivity quotient data; and
an output memory cell configured to store an inference.
14 . The ADI-PLU apparatus of claim 10 , wherein the ISN-MCU is configured to provide visual and auditory inferences;
wherein the one or more sense elements comprise audio and visual sensors.
15 . The ADI-PLU apparatus of claim 10 , wherein the ISN-MCU is configured to sense distance and imagery data, wherein the one or more sense elements comprise lidar sensors.
16 . The ADI-PLU apparatus of claim 10 , wherein the ISN-MCU is configured to identify chemicals, wherein the one or more sense elements comprise chemical sensors configured to convert chemical signatures into analyzable data.
17 . The ADI-PLU apparatus of claim 16 , wherein the chemical sensors comprise smell-based sensors.
18 . The ADI-PLU apparatus of claim 16 , wherein the chemical sensors comprise taste-based sensors.
19 . The ADI-PLU apparatus of claim 10 , wherein the ISN-MCU is configured to sense biological data, wherein the one or more sense elements comprise a biosensor.
20 . The ADI-PLU apparatus of claim 10 , wherein the ISN-MCU is configured to perform artificial intelligence (AI) model inference operations while simultaneously performing self-learning operations using techniques.