A Ternary Content Addressable Memory with STT-assisted Spin Orbit Torque for Hardware Accelerators

Główne pojęcia

This work presents a novel non-volatile spin transfer torque (STT) assisted spin-orbit torque (SOT) based ternary content addressable memory (TCAM) with 5 transistors and 2 magnetic tunnel junctions (MTJs) for hardware accelerators.

Streszczenie

The authors present a novel non-volatile spin transfer torque (STT) assisted spin-orbit torque (SOT) based ternary content addressable memory (TCAM) with 5 transistors and 2 magnetic tunnel junctions (MTJs) for hardware accelerators.

At the device-level, the write characteristics such as write error rate, time, and current are obtained using micromagnetic simulations. The array-level search and write performance are evaluated based on SPICE circuit simulations with layout extracted parasitics for bitcells, accounting for the impact of interconnect parasitics at the 7nm technology node. A search error rate of 3.9x10-11 is projected for exact search while considering various sources of variation.

The resolution of the approximate search operation is quantified under different scenarios to understand the achievable quality. The application-level performance and accuracy of the proposed design are evaluated and benchmarked against other state-of-the-art CAM designs in the context of a CAM-based recommendation system.

The proposed design eliminates the need for a magnetic field for the write operation, improving magnetic immunity compared to previous SOT-CAM designs. The authors perform a comprehensive study of the design in terms of write, exact search, and approximate search, optimizing the layout, array size, and search voltages to ensure accurate search operations.

Statystyki

The write energy is 1.74 pJ per bit for writing binary data (0/1) and 3.05 pJ per bit for writing 'X' for a 64x64 array.
The search delay for exact search is 12 ns and 5.78 ns for Vs=0.8V and Vs=1V, respectively, in a 64x128 array.
The search energy for exact search is 433 pJ and 366 pJ for Vs=0.8V and Vs=1V, respectively, in a 64x128 array.

Cytaty

"By using an STT-assisted write process, the design eliminates the need for a magnetic field for the write operation, therefore, improving magnetic immunity in comparison to the previous SOT-CAM design which used magnetic field-assisted write."
"We project a search error rate for exact search operations lower than 3.9x10-9%, when various sources of variation are considered."

Kluczowe wnioski z

A 5T-2MTJ STT-assisted Spin Orbit Torque based Ternary Content Addressable Memory for Hardware Accelerators

by Siri Narla, ... o arxiv.org 09-27-2024

https://arxiv.org/pdf/2409.17863.pdf

Głębsze pytania

How can the proposed design be further optimized to reduce the search energy and delay while maintaining the search accuracy?

To optimize the proposed 5T-2MTJ STT-assisted Spin Orbit Torque based Ternary Content Addressable Memory (TCAM) for reduced search energy and delay while maintaining search accuracy, several strategies can be employed:

Voltage Scaling: Lowering the search voltage (Vs) can significantly reduce energy consumption. However, this must be balanced with the need for sufficient voltage to maintain the discharge rate of the matchline (ML). Careful optimization of Vs can help achieve a balance between energy efficiency and search accuracy.

Improved Interconnect Design: The layout of the interconnects plays a crucial role in energy consumption and delay. Utilizing wider wires for bitlines and matchlines can reduce resistive losses and improve signal integrity, thereby decreasing delay. Additionally, optimizing the routing of interconnects to minimize capacitance can further enhance performance.

Dynamic Voltage and Frequency Scaling (DVFS): Implementing DVFS techniques can allow the TCAM to operate at lower voltages and frequencies during periods of low activity, thus saving energy without compromising performance during peak operations.

Enhanced Write Techniques: The write operation can be optimized by fine-tuning the STT and SOT currents to minimize write time while ensuring low write error rates. This can lead to faster search operations as the time taken to write data influences the overall performance of the TCAM.

Adaptive Search Algorithms: Implementing adaptive search algorithms that can dynamically adjust the search parameters based on the data characteristics can improve search efficiency. For instance, using approximate search techniques when exact matches are not critical can reduce energy and delay.

Integration of Advanced Sensing Techniques: Utilizing advanced sensing techniques, such as differential sensing or using multiple sense amplifiers, can enhance the detection of matches while reducing the energy required for search operations.

By focusing on these optimization strategies, the proposed TCAM design can achieve a significant reduction in search energy and delay while maintaining high search accuracy.

What are the potential challenges in scaling the proposed TCAM design to even smaller technology nodes, and how can they be addressed?

Scaling the proposed TCAM design to smaller technology nodes, such as 5nm or below, presents several challenges:

Increased Variability: As technology nodes shrink, process variations become more pronounced, leading to increased variability in device parameters such as threshold voltage and resistance. This can adversely affect the search error rate (SER) and overall reliability of the TCAM. To address this, robust design techniques such as adaptive biasing and error correction mechanisms can be implemented to mitigate the effects of variability.

Thermal Management: Smaller nodes often experience higher power densities, leading to thermal issues that can affect device performance and reliability. Implementing advanced thermal management techniques, such as improved heat dissipation designs and dynamic thermal throttling, can help manage these challenges.

Interconnect Limitations: As the dimensions of the TCAM cells decrease, the resistance and capacitance of interconnects can significantly impact performance. Utilizing advanced materials for interconnects, such as graphene or carbon nanotubes, can reduce resistive losses. Additionally, optimizing the layout to minimize interconnect lengths can help mitigate these issues.

Magnetic Stability: For the STT-assisted SOT devices, maintaining magnetic stability at smaller dimensions can be challenging due to thermal fluctuations. Employing materials with higher thermal stability and optimizing the design of the magnetic tunnel junctions (MTJs) can enhance performance at smaller nodes.

Integration with CMOS Technology: As the TCAM design scales, ensuring compatibility with existing CMOS technology becomes critical. Developing hybrid architectures that integrate TCAM with advanced CMOS processes can facilitate smoother scaling and improve overall performance.

By addressing these challenges through innovative design strategies and materials, the proposed TCAM can be effectively scaled to smaller technology nodes while maintaining performance and reliability.

How can the proposed TCAM design be integrated with other emerging memory technologies, such as ferroelectric FETs or spin-orbit torque magnetic memories, to further enhance the performance and energy efficiency of hardware accelerators?

Integrating the proposed TCAM design with other emerging memory technologies can significantly enhance performance and energy efficiency in hardware accelerators. Here are several approaches to achieve this integration:

Hybrid Memory Architectures: Combining the TCAM with ferroelectric FETs (FeFETs) can leverage the non-volatility and fast switching characteristics of FeFETs. By using FeFETs for storing configuration data or frequently accessed data, the overall energy consumption can be reduced, as FeFETs typically require lower write voltages compared to traditional SRAM or DRAM.

Multi-Level Storage: Integrating spin-orbit torque magnetic memories (SOT-MRAM) with the TCAM can enable multi-level storage capabilities. This allows the TCAM to store more than just binary or ternary data, potentially increasing the density and efficiency of the memory. The non-volatility of SOT-MRAM can also enhance data retention during power loss.

Shared Read/Write Paths: By designing shared read/write paths between the TCAM and other memory technologies, such as SOT-MRAM or FeFETs, the overall memory architecture can be simplified. This can lead to reduced latency and improved throughput, as data can be accessed and written more efficiently across different memory types.

Cross-Layer Optimization: Implementing cross-layer optimization techniques that consider the characteristics of both the TCAM and the integrated memory technologies can enhance performance. For instance, optimizing the search algorithms to take advantage of the unique properties of FeFETs or SOT-MRAM can lead to faster and more energy-efficient operations.

Advanced Error Correction: Integrating advanced error correction techniques that are tailored for the specific characteristics of both TCAM and emerging memory technologies can improve reliability and performance. This is particularly important in hybrid systems where different memory types may have varying error rates.

Dynamic Data Management: Utilizing dynamic data management strategies that intelligently allocate data between the TCAM and other memory types based on access patterns can optimize performance. For example, frequently accessed data can be stored in the faster TCAM, while less frequently accessed data can reside in the slower but more energy-efficient SOT-MRAM.

By exploring these integration strategies, the proposed TCAM design can be enhanced to deliver superior performance and energy efficiency, making it a valuable component in next-generation hardware accelerators.