EVE: Ephemeral Vector Engines
Khalid Al-Hawaj, Tuan Ta, Nick Cebry, Shady Agwa, Olalekan Afuye, Eric Hall, Courtney Golden, Alyssa Apsel, Christopher Batten
Abstract
There has been a resurgence of interest in vector architectures evident by recent adoption of vector extensions in mainstream instruction set architectures. Traditionally, vector engines leverage this abstraction by exploiting its inherent regularity to increase performance and efficiency. Recent work on SRAM-based compute-in-memory has shown promise in reducing the area overhead of these engines. In this work, we propose ephemeral vector engines (EVE) where we leverage SRAM-based compute-in-memory techniquesas well as bit-peripheral computations to facilitate efficient vector execution. EVE uses a novel approach of bit-hybrid execution, striking a balance between throughput and latency. Evaluated on the Rodinia and RiVEC benchmark suites, EVE achieves almost 8× speed-up compared to an out-of-order processor and 4.59× compared to an integrated vector unit. EVE achieves speed-ups comparable to an aggressive decoupled vector unit and increases the area-normalized performance by over 2 ×. By repurposing SRAM arrays in the L2 cache to create ephemeral vector execution units, EVE is able to efficiently achieve high performance while incurring as little as 11.7% area overhead.