Stochastic Associative Memory Read Scheduling to Avoid Partition Collisions
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Solution Overview
Problem
Stochastic associative memory systems face challenges in managing large-scale similarity searches due to compute and memory intensity, particularly in avoiding partition collisions during read operations, which affect query throughput and latency.
Innovation Solution
The system employs a binary sparse encoding and cluster-aware sharding of data across multiple Dual In-line Memory Modules (DIMMs) with a scheduler that optimizes read operations in two dimensions, allowing both row-wise and column-wise reads, and utilizes vector functional units to enhance query throughput and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored across multiple partitions in stochastic associative memory, then memory capacity and parallelism are improved, but partition collisions during read operations increase causing degraded query throughput and latency
Solution Approach 1:
The patent segments data into multiple partitions stored across different memory locations, and further segments the read operation into multiple stages. Each partition is accessed independently in parallel, but the overall read operation is divided into sequential stages that coordinate access across partitions, preventing collisions while maintaining parallelism benefits.
Solution Approach 2:
The patent performs preliminary actions by pre-computing partition indices and read schedules before actual query execution. The system pre-organizes data into partitions with assigned read orders, so that during query processing, the read operations are already optimized and coordinated to avoid partition collisions, reducing actual query latency.
2Productivity
If read operations are performed in parallel across multiple partitions, then query throughput is improved, but partition collisions increase causing read latency
Solution Approach 1:
The patent implements dynamic read scheduling where the system adapts the read operation sequence based on real-time partition availability and collision detection. The read schedule is not fixed but dynamically adjusted during query processing to optimize parallel access patterns and avoid collisions, balancing throughput and read speed.
Solution Approach 2:
The patent introduces a temporal dimension to the read operation scheduling by organizing reads in multiple stages across different time cycles. Instead of attempting all parallel reads simultaneously, the system distributes reads across time dimensions, allowing overlapping but non-conflicting access patterns that maintain high throughput without sacrificing read speed.
3Quantity of substance
If more partitions are used to store data, then memory utilization and parallelism are improved, but the complexity of managing read operations across partitions increases
Solution Approach 1:
The patent implements self-service mechanisms where each partition maintains metadata about its own data distribution and read requirements. The scheduling system queries this metadata to automatically generate collision-free read schedules without complex centralized control, reducing scheduling complexity while supporting arbitrary numbers of partitions.
Solution Approach 2:
The patent changes the parameter of partition identification from complex multi-dimensional indices to simplified hash-based identifiers. This parameter transformation enables straightforward partition mapping and collision detection, allowing the system to scale to many partitions without proportionally increasing scheduling complexity.
Data Source
AI summary
Binary sparse encoding of data can be used to reduce an amount of data read from the stochastic associative memory while processing a query. Read performance of the stochastic associated memory is optimized to enhance the query throughput by modifying access patterns to reduce the time to read the stochastic associated memory. Read performance of the stochastic associative memory can be further improved through the use of cluster aware sharding and replication for parallelized similarity search. Clusters are partitioned across multiple Dual In-line Memory Modules (DIMMs), each DIMM including stochastic associative memory, to achieve maximum latency advantage.