Analog Neural Memory Concurrent Write Verify Operations
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Solution Overview
Problem
Current hardware technologies for artificial neural networks lack adequate efficiency in terms of cost and energy consumption, particularly due to the high complexity and bulkiness of CMOS-implemented synapses, which hinder high-performance information processing.
Innovation Solution
The development of non-volatile memory arrays as synapses in analog neural networks, enabling concurrent write and verify operations, which reduces timing overhead and allows for efficient storage and tuning of synaptic weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If CMOS analog circuits are used for synapses, then neural network functionality is achieved, but the synapses become too bulky for high connectivity
Solution Approach 1:
The patent replaces CMOS analog circuits with resistive memory devices (such as RRAM or PCM) to implement synapses. This substitution transitions from a complex circuit-based implementation to a simpler device-based implementation, dramatically reducing the area required per synapse while maintaining the ability to perform analog multiplication and accumulation operations essential for neural network functionality.
Solution Approach 2:
The patent changes the fundamental operating parameter from digital voltage levels in CMOS to analog resistance values in memory devices. By programming the resistance of memory cells to represent synaptic weights, the system achieves high-precision analog computation with much smaller device footprints, enabling high connectivity ratios required for efficient neural networks.
2Manufacturing precision
If sequential write and verify operations are performed, then weight storage accuracy is ensured, but timing overhead increases
Solution Approach 1:
The patent applies preliminary actions by performing verify operations on previously programmed weights before attempting to program new weights. This ensures that the memory device is ready to accept new programming and that previous weights are properly stored, thereby reducing retries and timing overhead while maintaining weight storage accuracy.
Solution Approach 2:
The patent enables continuous weight updates by overlapping verify and write operations. While verify operations check previously stored weights, new weight programming can proceed in parallel on different memory cells or blocks, eliminating idle time and maintaining continuous productive action in the weight tuning process.
3Productivity
If high connectivity between neurons is achieved, then computational parallelism increases, but hardware complexity and cost increase
Solution Approach 1:
The patent transitions from a planar 2D arrangement of neurons and synapses to a 3D crossbar architecture where synapses are formed at the intersections of word lines and bit lines. This dimensional change enables N×M connectivity with only N+M physical connections, dramatically reducing hardware complexity while achieving high computational parallelism through the crossbar's inherent matrix multiplication capability.
Data Source
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AI summary
Numerous embodiments of analog neural memory systems that enable concurrent write and verify operations are disclosed. In some embodiments, concurrent operations occur among different banks of memory. In other embodiments, concurrent operations occur among different blocks of memory, where each block comprises two or more banks of memory. The embodiments substantially reduce the timing overhead for weight writing and verifying operations in analog neural memory systems.