HURRY: A Highly Utilized, Reconfigurable, and Multifunctional ReRAM-based In-situ Accelerator for Efficient CNN Inference

מושגי ליבה

HURRY, a reconfigurable and multifunctional ReRAM-based in-situ accelerator, enhances spatial and temporal utilization of ReRAM arrays to achieve significant performance, energy, and area efficiency improvements over existing ReRAM-based accelerators.

תקציר

The paper introduces HURRY, a reconfigurable and multifunctional ReRAM-based in-situ accelerator designed to address the spatial and temporal underutilization issues in conventional ReRAM-based accelerators.
Key highlights:

Spatial Utilization: HURRY uses a block activation scheme (BAS) to dynamically resize and activate portions of the large 512x512 ReRAM arrays, balancing spatial utilization and peripheral overhead.
Temporal Utilization: HURRY incorporates functional blocks (FBs) for various CNN operations like convolution, ReLU, max pooling, and softmax, reducing data movement between ReRAM and computing units.
System-level Scheduling: HURRY employs fine-grained pipelining of FBs and strategic data mapping to further optimize performance.
Evaluation Results: Compared to existing baselines, HURRY achieves up to 3.35x speedup, 5.72x higher energy efficiency, and 7.91x greater area efficiency for CNN inference tasks.

The paper demonstrates that HURRY's reconfigurability and multifunctionality effectively address the spatial and temporal underutilization issues in conventional ReRAM-based accelerators, leading to significant improvements in overall system efficiency.

סטטיסטיקה

As the unit array size increases from 128x128 to 512x512, the ReRAM spatial utilization drops from 99% to 57%.
Compared to 16 units of 128x128 arrays, a single 512x512 array has a 3.7x lower chip area and 3.4x lower ADC power consumption.
ISAAC exhibits up to 48% of runtime spent on data movement between ReRAM arrays and computing units.

ציטוטים

"HURRY uses a block activation scheme (BAS) to dynamically resize and activate portions of the large 512x512 ReRAM arrays, balancing spatial utilization and peripheral overhead."
"HURRY incorporates functional blocks (FBs) for various CNN operations like convolution, ReLU, max pooling, and softmax, reducing data movement between ReRAM and computing units."
"Compared to existing baselines, HURRY achieves up to 3.35x speedup, 5.72x higher energy efficiency, and 7.91x greater area efficiency for CNN inference tasks."

תובנות מפתח מזוקקות מ:

HURRY: Highly Utilized, Reconfigurable ReRAM-based In-situ Accelerator with Multifunctionality

by Hery Shin, J... ב- arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16640.pdf

שאלות מעמיקות

How can the reconfigurability and multifunctionality of HURRY's ReRAM arrays be extended to support other types of neural network architectures beyond CNNs?

The reconfigurability and multifunctionality of HURRY's ReRAM arrays can be extended to support other neural network architectures, such as recurrent neural networks (RNNs) and transformers, by adapting the functional blocks (FBs) to accommodate the specific computational requirements of these architectures. For instance, RNNs often require operations like matrix-vector multiplications and recurrent connections, which can be implemented using the existing in-situ multiply-accumulate (MAC) capabilities of the ReRAM arrays. By designing additional FBs that handle recurrent connections and state management, HURRY can efficiently process RNN workloads.
Moreover, for transformer architectures, which rely heavily on attention mechanisms, HURRY can integrate specialized FBs that perform the necessary computations for self-attention and multi-head attention. This could involve implementing mechanisms for key-value pair storage and retrieval directly within the ReRAM arrays, leveraging their high density and low latency for efficient data access. The hybrid mapping scheme (HMS) can also be adapted to optimize data flow for these architectures, ensuring that the ReRAM arrays are utilized effectively for both weight and input stationary dataflows.
In summary, by extending the design of functional blocks and optimizing data mapping strategies, HURRY's ReRAM arrays can be reconfigured to support a broader range of neural network architectures, enhancing their versatility and performance across various machine learning tasks.

What are the potential challenges and trade-offs in further increasing the size of the ReRAM arrays in HURRY, and how could they be addressed?

Increasing the size of the ReRAM arrays in HURRY presents several challenges and trade-offs, primarily related to spatial and temporal utilization, peripheral overhead, and power consumption. One significant challenge is the diminishing returns on spatial utilization as array sizes increase. Larger arrays may lead to lower spatial utilization rates due to the fixed nature of CNN kernels, resulting in underutilized memory cells. This issue can be addressed by implementing dynamic resizing techniques that allow the ReRAM arrays to adapt to the specific requirements of different layers, thereby maintaining high spatial utilization.
Another challenge is the increased peripheral overhead associated with larger arrays, particularly concerning analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). As the size of the ReRAM arrays grows, the power and area consumed by these peripherals can significantly impact overall energy efficiency. To mitigate this, HURRY could incorporate more efficient ADC/DAC designs or utilize shared peripheral resources across multiple ReRAM arrays, thereby reducing the overall power and area overhead.
Additionally, larger arrays may introduce increased latency due to longer signal propagation times and more complex control logic. This can be addressed by optimizing the architecture for parallel processing and enhancing the controller's efficiency to manage larger arrays effectively.
In conclusion, while increasing the size of ReRAM arrays in HURRY can enhance computational capacity, careful consideration of spatial utilization, peripheral overhead, and latency is essential. By implementing dynamic resizing, optimizing peripheral designs, and enhancing control logic, these challenges can be effectively managed.

Given the advancements in ReRAM technology, how might HURRY's design and performance evolve in the future as ReRAM characteristics continue to improve?

As ReRAM technology advances, HURRY's design and performance are likely to evolve significantly, driven by improvements in ReRAM characteristics such as speed, endurance, density, and energy efficiency. One potential evolution is the integration of multi-level cell (MLC) ReRAM technology, which would allow each cell to store more than one bit of information. This could dramatically increase the storage density of the ReRAM arrays, enabling HURRY to handle larger models and datasets without a proportional increase in physical area.
Furthermore, advancements in switching speed and endurance could enhance the performance of HURRY by reducing latency in read and write operations. This would allow for faster data processing and improved throughput, particularly beneficial for real-time applications in edge computing and mobile devices. The design could also incorporate more sophisticated error correction mechanisms to maintain data integrity in MLC configurations, ensuring reliable operation over extended periods.
Additionally, as ReRAM technology becomes more energy-efficient, HURRY could leverage these improvements to further enhance its energy efficiency metrics. This would be particularly advantageous for applications requiring low power consumption, such as IoT devices and battery-operated systems.
In summary, the future evolution of HURRY's design and performance will likely be characterized by the adoption of multi-level ReRAM technology, enhanced switching speeds, improved endurance, and greater energy efficiency. These advancements will enable HURRY to support more complex neural network architectures and applications while maintaining high performance and efficiency.