The general-purpose approximate nature of neural network (NN) based accelerators has the potential to sustain the historic energy and performance improvements of computing systems. We propose the use of NN-based accelerators to approximate mathematical functions in the GNU C Library (glibc) that commonly occur in application benchmarks. Using our NN-based approach to approximate cos, exp, log, pow, and sin we achieve an average energy-delay product (EDP) that is 68x lower than that of traditional glibc execution. In applications, our NN-based approach has an EDP 78% of that of traditional execution at the cost of an average mean squared error (MSE) of 1.56.
Abstract-Machine learning is becoming pervasive; decades of research in neural network computation is now being leveraged to learn patterns in data and perform computations that are difficult to express using standard programming approaches. Recent work has demonstrated that custom hardware accelerators for neural network processing can outperform software implementations in both performance and power consumption. However, there is neither an agreed-upon interface to neural network accelerators nor a consensus on neural network hardware implementations. We present a generic set of software/hardware extensions, X-FILES, that allow for the generalpurpose integration of feedforward and feedback neural network computation in applications. The interface is independent of the network type, configuration, and implementation. Using these proposed extensions, we demonstrate and evaluate an example dynamically allocated, multi-context neural network accelerator architecture, DANA. We show that the combination of X-FILES and our hardware prototype, DANA, enables generic support and increased throughput for neural-networkbased computation in multi-threaded scenarios. These diverse implementations and usage cases drive fascinating innovation. As diversity increases, however, a gap is developing between these innovations and the state of today's hardware and software.
I. INTRODUCTION1 As such it is worth con-
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.