CAMEL: Co-Designing AI Models and Embedded DRAMs for Efficient On-Device
Learning
- URL: http://arxiv.org/abs/2305.03148v3
- Date: Fri, 22 Dec 2023 19:22:05 GMT
- Title: CAMEL: Co-Designing AI Models and Embedded DRAMs for Efficient On-Device
Learning
- Authors: Sai Qian Zhang, Thierry Tambe, Nestor Cuevas, Gu-Yeon Wei, David
Brooks
- Abstract summary: Training AI on resource-limited devices poses significant challenges due to the demanding computing workload and the substantial memory consumption and data access required by deep neural networks (DNNs)
We propose utilizing embedded dynamic random-access memory (eDRAM) as the primary storage medium for transient training data.
We present a highly efficient on-device training engine named textitCAMEL, which leverages eDRAM as the primary on-chip memory.
- Score: 8.339901980070616
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: On-device learning allows AI models to adapt to user data, thereby enhancing
service quality on edge platforms. However, training AI on resource-limited
devices poses significant challenges due to the demanding computing workload
and the substantial memory consumption and data access required by deep neural
networks (DNNs). To address these issues, we propose utilizing embedded dynamic
random-access memory (eDRAM) as the primary storage medium for transient
training data. In comparison to static random-access memory (SRAM), eDRAM
provides higher storage density and lower leakage power, resulting in reduced
access cost and power leakage. Nevertheless, to maintain the integrity of the
stored data, periodic power-hungry refresh operations could potentially degrade
system performance.
To minimize the occurrence of expensive eDRAM refresh operations, it is
beneficial to shorten the lifetime of stored data during the training process.
To achieve this, we adopt the principles of algorithm and hardware co-design,
introducing a family of reversible DNN architectures that effectively decrease
data lifetime and storage costs throughout training. Additionally, we present a
highly efficient on-device training engine named \textit{CAMEL}, which
leverages eDRAM as the primary on-chip memory. This engine enables efficient
on-device training with significantly reduced memory usage and off-chip DRAM
traffic while maintaining superior training accuracy. We evaluate our CAMEL
system on multiple DNNs with different datasets, demonstrating a $2.5\times$
speedup of the training process and $2.8\times$ training energy savings than
the other baseline hardware platforms.
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