Kevin Weston

Vahid Janfaza, Kevin Weston, Moein Razavi et al.

Deep Neural Networks (DNN) are computationally intensive to train. It consists of a large number of multidimensional dot products between many weights and input vectors. However, there can be significant similarity among input vectors. If one input vector is similar to another, its computations with the weights are similar to those of the other and, therefore, can be skipped by reusing the already-computed results. We propose a novel scheme, called MERCURY, to exploit input similarity during DNN training in a hardware accelerator. MERCURY uses Random Projection with Quantization (RPQ) to convert an input vector to a bit sequence, called Signature. A cache (MCACHE) stores signatures of recent input vectors along with the computed results. If the Signature of a new input vector matches that of an already existing vector in the MCACHE, the two vectors are found to have similarities. Therefore, the already-computed result is reused for the new vector. To the best of our knowledge, MERCURY is the first work that exploits input similarity using RPQ for accelerating DNN training in hardware. The paper presents a detailed design, workflow, and implementation of the MERCURY. Our experimental evaluation with twelve different deep learning models shows that MERCURY saves a significant number of computations and speeds up the model training by an average of 1.97X with an accuracy similar to the baseline system.

Kevin Weston, Vahid Jafanza, Arnav Kansal et al.

Computer applications are continuously evolving. However, significant knowledge can be harvested from a set of applications and applied in the context of unknown applications. In this paper, we propose to use the harvested knowledge to tune hardware configurations. The goal of such tuning is to maximize hardware efficiency (i.e., maximize an applications performance while minimizing the energy consumption). Our proposed approach, called FORECASTER, uses a deep learning model to learn what configuration of hardware resources provides the optimal energy efficiency for a certain behavior of an application. During the execution of an unseen application, the model uses the learned knowledge to reconfigure hardware resources in order to maximize energy efficiency. We have provided a detailed design and implementation of FORECASTER and compared its performance against a prior state-of-the-art hardware reconfiguration approach. Our results show that FORECASTER can save as much as 18.4% system power over the baseline set up with all resources. On average, FORECASTER saves 16% system power over the baseline setup while sacrificing less than 0.01% of overall performance. Compared to the prior scheme, FORECASTER increases power savings by 7%.