thông tin chi tiết - Technology - # Optimizing DNN Architecture and CiM Hardware

CiMNet: Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware

Q: How can CiMNet's approach be applied to other types of hardware architectures?

CiMNet's approach of joint optimization for DNN architecture and hardware configurations can be adapted to various types of hardware architectures by modifying the elastic configuration parameters based on the specific characteristics of the target architecture. For instance, in a different type of compute-in-memory (CiM) architecture or even traditional Von-Neumann architectures, one could adjust parameters related to memory hierarchy, bandwidth, compute granularity, and micro-architecture to suit the hardware constraints. By incorporating these adjustments into the joint search framework along with elastic model parameters, it is possible to find optimal sub-networks and hardware configurations that maximize performance efficiency for different architectures.

Q: What potential drawbacks or limitations might arise from joint optimization of sub-networks and hardware configurations?

One potential drawback of joint optimization could be the increased complexity and computational cost associated with searching over a larger design space that includes both network architecture variations and diverse hardware configurations. This may lead to longer search times and higher resource requirements for training accuracy and cycle count predictors. Additionally, there could be challenges in accurately modeling the interplay between network attributes and hardware specifications which may result in suboptimal solutions if not properly addressed. Furthermore, another limitation could stem from the need for extensive domain expertise in both neural networks and hardware design to effectively navigate through the joint optimization process. Without a deep understanding of how different architectural choices impact performance metrics on specific hardware setups, it may be challenging to derive meaningful insights from the co-search framework.

Q: How does dataflow impact overall efficiency of DNN execution on CiM hardware?

Dataflow plays a crucial role in determining the efficiency of DNN execution on Compute-In-Memory (CiM) hardware by influencing factors such as data transfer rates, resource utilization, latency reduction, and overall system throughput. Optimal dataflow organization ensures that input feature maps (IFMs), output feature maps (OFMs), weights data are efficiently managed within memory arrays while maximizing compute resource utilization. By strategically dividing dimensions like batch size, height/width/depth dimensions into spatial tiles distributed across multiple compute nodes working concurrently alongside temporal chunks fitting into subarray memory capacities optimally minimizes data transfers while enhancing parallel processing capabilities. This results in reduced latency during DNN execution as fewer read/write operations are needed due to efficient chunking strategies based on physical constraints imposed by CiM architecture. In essence, an effective dataflow strategy aligns computational workloads with available resources leading to improved performance metrics such as lower cycle counts per inference task which ultimately enhances overall efficiency when executing deep neural networks on CiM-based systems.

Khái niệm cốt lõi

CiMNet presents a framework for joint optimization of sub-networks and hardware configurations, enhancing DNN efficiency.

Tóm tắt

CiMNet addresses the challenge of matching memory hierarchy to neural network attributes, leading to suboptimal systems. By jointly optimizing sub-networks and hardware configurations, CiMNet achieves superior performance. The framework considers bandwidth, processing element size, and memory size in CiM architectures. Experimental results show significant performance improvements with optimized architecture and hardware configuration.

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Thống kê

Specifically, for similar ImageNet classification accuracy as baseline ViT-B, optimizing only the model architecture increases performance (or reduces workload execution time) by 1.7× while optimizing for both the model architecture and hardware configuration increases it by 3.1×.

Trích dẫn

"With the recent growth in demand for large-scale deep neural networks, compute in-memory (CiM) has come up as a prominent solution to alleviate bandwidth and on-chip interconnect bottlenecks."
"We believe CiMNet provides a novel paradigm and framework for co-design to arrive at near-optimal and synergistic DNN algorithms and hardware."

Thông tin chi tiết chính được chắt lọc từ

CiMNet

by Souvik Kundu... lúc arxiv.org 03-19-2024

https://arxiv.org/pdf/2402.11780.pdf

Yêu cầu sâu hơn

How can CiMNet's approach be applied to other types of hardware architectures?

CiMNet's approach of joint optimization for DNN architecture and hardware configurations can be adapted to various types of hardware architectures by modifying the elastic configuration parameters based on the specific characteristics of the target architecture. For instance, in a different type of compute-in-memory (CiM) architecture or even traditional Von-Neumann architectures, one could adjust parameters related to memory hierarchy, bandwidth, compute granularity, and micro-architecture to suit the hardware constraints. By incorporating these adjustments into the joint search framework along with elastic model parameters, it is possible to find optimal sub-networks and hardware configurations that maximize performance efficiency for different architectures.

What potential drawbacks or limitations might arise from joint optimization of sub-networks and hardware configurations?

One potential drawback of joint optimization could be the increased complexity and computational cost associated with searching over a larger design space that includes both network architecture variations and diverse hardware configurations. This may lead to longer search times and higher resource requirements for training accuracy and cycle count predictors. Additionally, there could be challenges in accurately modeling the interplay between network attributes and hardware specifications which may result in suboptimal solutions if not properly addressed.
Furthermore, another limitation could stem from the need for extensive domain expertise in both neural networks and hardware design to effectively navigate through the joint optimization process. Without a deep understanding of how different architectural choices impact performance metrics on specific hardware setups, it may be challenging to derive meaningful insights from the co-search framework.

How does dataflow impact overall efficiency of DNN execution on CiM hardware?

Dataflow plays a crucial role in determining the efficiency of DNN execution on Compute-In-Memory (CiM) hardware by influencing factors such as data transfer rates, resource utilization, latency reduction, and overall system throughput. Optimal dataflow organization ensures that input feature maps (IFMs), output feature maps (OFMs), weights data are efficiently managed within memory arrays while maximizing compute resource utilization.
By strategically dividing dimensions like batch size, height/width/depth dimensions into spatial tiles distributed across multiple compute nodes working concurrently alongside temporal chunks fitting into subarray memory capacities optimally minimizes data transfers while enhancing parallel processing capabilities. This results in reduced latency during DNN execution as fewer read/write operations are needed due to efficient chunking strategies based on physical constraints imposed by CiM architecture.
In essence, an effective dataflow strategy aligns computational workloads with available resources leading to improved performance metrics such as lower cycle counts per inference task which ultimately enhances overall efficiency when executing deep neural networks on CiM-based systems.