Computing system
A system includes a transceiver to communicate with a predetermined target; one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target.
1. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target;
an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target; and
a parser that receives classical specification and data and determines if a portion of such specification runs on a quantum computer, and if so maps classical specification to quantum algorithm and selects code execution from one or more quantum computers, one or more classical processor, one or more graphical processing units (GPUs), or one or more neuromorphic processors.
2. The system of claim 1 , wherein the transceiver comprises a 5G or 6G cellular transceiver.
3. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
a beam sweeping module controlling the antenna in accordance with one of: a service level agreement, a performance requirement, a traffic distribution data, a networking requirement or prior beam sweeping history; and
an edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
4. The system of claim 3 , wherein the beam sweeping is directed at a group of autonomous vehicles, a group of virtual reality devices, or a group of devices performing similar functions.
5. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target; and
a neural network or a learning machine coupled to a control plane, a management
plane, and a data plane to optimize 5G parameters.
6. The system of claim 5 , comprising a quantum computer coupled to the edge processing module.
7. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target, wherein the edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
8. The system of claim 7 , wherein the processor calibrates a connection by analyzing RSSI and TSSI and moves the antennas until predetermined cellular parameters are reached.
9. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target; and
a cloud trained neural network whose network parameters are down-sampled or filter count reduced before transferring to the edge neural network.
10. The system of claim 9 , wherein the edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
11. The system of claim 9 , wherein the edge processing module and the antenna are enclosed in a housing or shipping container, or the edge processing module is in a separate shipping container adjacent the antenna.
12. The system of claim 9 , wherein the edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
13. The system of claim 9 , comprising one or more cameras and sensors in the housing to capture security information.
14. The system of claim 9 , comprising edge sensors including LIDAR and RADAR.
15. The system of claim 9 , comprising a camera for individual identity identification.
16. The system of claim 9 , wherein the edge processing module streams data to the predetermined target to minimize loading the target.
17. The system of claim 9 , comprising an edge learning machine in a housing or shipping container to provide local edge processing for Internet-of-Things (TOT) sensors.
18. The system of claim 17 , wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task.
19. The system of claim 9 comprising a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
20. The system of claim 9 , wherein the edge processing module comprises a learning machine, a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).