Session-Based Recommendation Layer Profiling on FPGA
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Solution Overview
Problem
Conventional recommendation models struggle to estimate performance optimally for session-based recommendation systems, particularly in high-computational workloads and diverse computation scenarios, leading to suboptimal implementation of layers on available hardware.
Innovation Solution
A system and method for estimating performance of session-based recommendation model layers on FPGA by analyzing and profiling each layer, determining network bandwidth, and deploying optimal layers on CPUs, GPUs, or FPGA based on estimated performance and user-defined constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional recommendation models are used for session-based recommendations, then implementation is simpler, but performance is suboptimal under high computational workloads
Solution Approach 1:
The patent segments the recommendation model into multiple independent layers (graph creation layer, GNN layer, position embedding layer, attention layer, and scoring layer). Each layer can be independently profiled, estimated, and deployed on optimal hardware, enabling parallel processing and improving overall recommendation rate while managing complexity through modular design.
Solution Approach 2:
The patent introduces a new dimension of hardware heterogeneity by deploying different model layers across multiple hardware platforms (CPU, GPU, FPGA). This dimensional expansion allows the system to leverage the strengths of each hardware type for specific layers, achieving superior performance under high computational workloads without proportionally increasing overall system complexity.
2Productivity
If more hardware resources are allocated to process all layers, then throughput increases, but latency increases and cost increases
Solution Approach 1:
The patent applies local quality by assigning different hardware platforms to different model layers based on their specific computational characteristics. Each layer is deployed on hardware that best suits its requirements (e.g., GPU for compute-intensive layers, FPGA for layers requiring low latency), optimizing throughput for each layer without uniformly increasing resources across the entire system, thereby minimizing overall latency.
Solution Approach 2:
The patent performs preliminary action by profiling and estimating the performance characteristics of each layer before deployment. This advance analysis allows the system to pre-determine the optimal hardware assignment for each layer, enabling efficient resource allocation that maximizes throughput while minimizing latency from the outset rather than reacting to performance bottlenecks.
3Adaptability or versatility
If existing state of the art techniques are used, then implementation is straightforward, but performance estimation of layers is lacking for selecting optimal hardware
Solution Approach 1:
The patent implements preliminary action through a comprehensive profiling and estimation system that analyzes each layer's computational requirements, memory access patterns, and performance characteristics before deployment. This advance estimation enables informed hardware selection for each layer, providing adaptability in hardware assignment while the automated nature of the estimation process prevents excessive system complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the profiling and estimation system continuously monitors layer performance metrics and uses this information to optimize hardware assignments. The estimated performance data feeds back into the deployment decision process, enabling adaptive hardware selection that balances versatility with manageable system complexity through data-driven decisions.
Data Source
AI summary
This disclosure relates generally to method and system to estimate performance of session based recommendation model layers on FPGA. Profiling is easy to perform on software based platforms such as a CPU and a GPU which have development frameworks and tool sets but on systems such as a FPGA, implementation risks are higher and important to model the performance prior to implementation. The disclosed method analyses a session based recommendation (SBR) model layers for performance estimation. Further, a network bandwidth is determined to process each layer of the SBR model based on dimensions. Performance of each layer of the SBR model is estimated at a predefined frequency by creating a layer profile comprising a throughput and a latency in one or more batches. Further, the method deploys an optimal layer on at least one of a heterogeneous hardware based on the estimated performance of each layer profile on the FPGA.