Associative Memristive Memory for Approximate Computing in GPUs

Using associative memories to enable computing-with-memory is a promising approach to improve energy efficiency. Associative memories can be tightly coupled with processing elements to restore and later recall function responses for a subset of input values. This approach avoids the actual function execution on the processing element to save on energy. The challenge, however, is to reduce the energy consumption of associative memory modules themselves. Here we address the challenge of designing ultra-low-power associative memories. We use memristive parts for memory implementation and demonstrate the energy saving potential of integrating associative memristive memory (AMM) into graphics processing units (GPUs). To reduce the energy consumption of AMM modules, we leverage approximate computing which benefits from application-level tolerance to errors: We employ voltage overscaling on AMM modules which deliberately relaxes its searching criteria to approximately match stored patterns within a 2 bit Hamming distance of the search pattern. This introduces some errors to the computation that are tolerable for target applications. We further reduce the energy consumption by employing purely resistive crossbar architectures for AMM modules. To evaluate the proposed architecture, we integrate AMM modules with floating point units in an AMD Southern Islands GPU and run four image processing kernels on an AMM-integrated GPU. Our experimental results show that employing AMM modules reduces energy consumption of running these kernels by 23%-45%, compared to a baseline GPU without AMM. The image processing kernels tolerate errors resulting from approximate search operations, maintaining an acceptable image quality, i.e., a PSNR above 30 dB.