DA-VinCi: A Deep-Learning Accelerator Overlay Using In-Memory Computing

The matrix operations that underpin today’s deep learning models are routinely implemented in Single Instruction Multiple Data (SIMD) domain specific accelerators. SIMD accelerators including GPUs and array processors can effectively leverage parallelism in models that are compute-bound, but their effectiveness can be diminished for models that are memory-bound. Processing-in-Memory (PIM) architectures are being explored to provide better energy efficiency and scalable performance for these memory-bound models. Modern Field Programmable Gate Arrays (FPGAs) feature hundreds of megabits of Static Random Access Memory (SRAM) distributed across the device as disaggregated memory resources. This makes FPGAs ideal programmable platforms for developing custom Processor In/Near Memory accelerators. Several PIM array-based accelerator designs have been proposed to leverage this substantial internal bandwidth. However, results reported to date show the FPGA based PIM architectures operating at system clock frequencies well below a chips Block-RAM (BRAM) Fmax clock frequency. Results also show that the compute densities of the designs do not scale linearly with BRAM densities. These results indicate that FPGA PIM architectures will never be competitive with their custom Application-Specific Integrated Circuit (ASIC) counterparts. In this article, we introduce DA-VinCi, a D eep-Learning A ccelerator O v erlay using In -Memory C omput i ng. DA-VinCi is the first scalable FPGA based PIM deep-learning accelerator overlay capable of clocking at the maximum frequency of a device’s BRAM. Further, the architecture of DA-VinCi allows the number of compute units to scale linearly up to the maximum capacity of a devices BRAM, and at the maximum clock frequency of the BRAM. The DA-VinCi overlay has a programmable Instruction Set Architecture (ISA) that allows the same synthesized design to provide low-latency inferencing of a range of memory-bound deep-learning models, including Multilayer Perceptrons, Recurrent Neural Network, Long Short-Term Memory, and Gated Recurrent Unit networks. The scalability and high clocking frequency of DA-VinCi is achieved through a new Processor In Memory (PIM) tile architecture and a highly scalable system-level framework. We present results showing DA-VinCi linearly scaling the number of Processing Elements (PEs) to 100% of the BRAM capacity (over 60K PEs) on an Alveo U55 clocking at 737 MHz, the chips BRAM Fmax. We provide comparative studies on inference latency across multiple deep-learning applications that show DA-VinCi achieves up to a 201 \(\times\) improvement over a state-of-the-art PIM overlay accelerator, up to 87 \(\times\) improvement over existing PIM-based FPGA accelerators, and up to 57 \(\times\) improvement over custom deep-learning accelerators on FPGAs.