Reconfigurable Neuromorphic Processor Interconnection Scheme
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Solution Overview
Problem
Modern processors face challenges in executing complex instructions efficiently, particularly those requiring significant execution time and resources such as floating-point operations and data moves, which can hinder overall processor performance.
Innovation Solution
The implementation of a reconfigurable neuromorphic processor that mimics biological neural networks, allowing for adaptive learning through iterative adjustment of synapse weights, enabling efficient execution of complex tasks like facial recognition and object detection without the need for explicit programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional processors execute complex instructions (floating-point operations, data moves), then computational tasks can be performed, but processor performance is hindered due to significant execution time and resource consumption
Solution Approach 1:
The patent implements a reconfigurable processor architecture where the instruction set can be dynamically changed at runtime. The processor can switch between different instruction sets (e.g., from complex floating-point operations to simpler integer operations) based on the specific computational task, thereby optimizing execution efficiency for different workloads while reducing overall execution time
Solution Approach 2:
The processor allows changing of instruction set parameters and architectural characteristics during operation. By modifying the instruction set architecture to match the requirements of specific algorithms (e.g., using simplified instructions for machine learning workloads), the system achieves better performance without the overhead of complex traditional instructions
2Adaptability or versatility
If reconfigurable neuromorphic processor is implemented to enable adaptive learning, then machines can learn from training data and execute complex tasks more efficiently, but device complexity increases due to synapse arrays, neuron arrays, and routing channels
Solution Approach 1:
The processor is divided into multiple independent synapse arrays and neuron arrays that can be independently configured and trained. Each synapse array can store weights for specific neural network layers, allowing the complex learning task to be distributed across multiple manageable units rather than requiring a monolithic complex structure
Solution Approach 2:
The synapse arrays and neuron arrays are designed to be multi-functional, capable of performing both traditional computational tasks and neuromorphic learning operations. The same hardware structures can be reconfigured to implement different neural network architectures (e.g., convolutional networks, recurrent networks), reducing the need for specialized complex hardware for each function
Data Source
AI summary
Systems and methods for an interconnection scheme for reconfigurable neuromorphic hardware are disclosed. A neuromorphic processor may include a plurality of corelets, each corelet may include a plurality of synapse arrays and a neuron array. Each synapse array may include a plurality of synapses and a synapse array router coupled to synapse outputs in a synapse array. Each synapse may include a synapse input, synapse output; and a synapse memory. A neuron array may include a plurality of neurons, each neuron may include a neuron input and a neuron output. Each synapse array router may include a first logic to route one or more of the synapse outputs to one or more of the neuron inputs.