A data-driven energy efficient and flexible compute fabric architecture: For adaptive computing applied to ULSI of FFT

In this work, we investigate architectures that can provide the benefits of dedicated hardware implementations and the flexibility of software defined environments. We call this new approach a data defined environment, in which hardware and software scales together based on workload variability to provide state-of-the-art hardware energy-efficiency. An integrated architecture for rapidly implementing efficient large-scale Digital Signal Processing (DSP) functions is presented. The target DSP functions are represented by an application space with one or more dimensions and several ensembles of Adaptive Computing Fabrics (ACF). It is shown that the proposed fabric allows achieving deterministic performance exceeding 245GOPs/mW for data workload characterized by high dynamic variability such as FFT of various size including 64, 128, 512, 1,024, 2,048, 4,098, 8,192, 262,144 and 746,496. Experimental results show improvements on the order of 1000× in power efficiency when compared to published alternatives for several applications, including H.265 High-Efficiency Video Coding (HEVC), Bluetooth, LTE, xDSL/DVB, WLAN and mm-Waves Wireless Personal Networks (WPAN).