RACE-IT: A Reconfigurable Analog Computing Engine for In-Memory Transformer Acceleration
This addresses the problem of high computational and energy costs for Transformer models in AI applications, offering a novel hardware solution with broad adaptability.
The paper tackles the challenge of accelerating Transformer models, which require significant computational resources and memory, by introducing RACE-IT, a reconfigurable analog computing engine for in-memory acceleration, resulting in performance increases of 453x and 15x and energy reductions of 354x and 122x over GPUs and existing accelerators.
Transformer models represent the cutting edge of Deep Neural Networks (DNNs) and excel in a wide range of machine learning tasks. However, processing these models demands significant computational resources and results in a substantial memory footprint. While In-memory Computing (IMC)offers promise for accelerating Vector-Matrix Multiplications(VMMs) with high computational parallelism and minimal data movement, employing it for other crucial DNN operators remains a formidable task. This challenge is exacerbated by the extensive use of complex activation functions, Softmax, and data-dependent matrix multiplications (DMMuls) within Transformer models. To address this challenge, we introduce a Reconfigurable Analog Computing Engine (RACE) by enhancing Analog Content Addressable Memories (ACAMs) to support broader operations. Based on the RACE, we propose the RACE-IT accelerator (meaning RACE for In-memory Transformers) to enable efficient analog-domain execution of all core operations of Transformer models. Given the flexibility of our proposed RACE in supporting arbitrary computations, RACE-IT is well-suited for adapting to emerging and non-traditional DNN architectures without requiring hardware modifications. We compare RACE-IT with various accelerators. Results show that RACE-IT increases performance by 453x and 15x, and reduces energy by 354x and 122x over the state-of-the-art GPUs and existing Transformer-specific IMC accelerators, respectively.