Rajeev Balasubramonian

Bio: Rajeev Balasubramonian is an academic researcher from University of Utah. The author has contributed to research in topics: Cache & Memory controller. The author has an hindex of 43, co-authored 111 publications receiving 8294 citations. Previous affiliations of Rajeev Balasubramonian include Hewlett-Packard & University of Rochester.

ISAAC: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars

Abstract: A number of recent efforts have attempted to design accelerators for popular machine learning algorithms, such as those involving convolutional and deep neural networks (CNNs and DNNs). These algorithms typically involve a large number of multiply-accumulate (dot-product) operations. A recent project, DaDianNao, adopts a near data processing approach, where a specialized neural functional unit performs all the digital arithmetic operations and receives input weights from adjacent eDRAM banks.This work explores an in-situ processing approach, where memristor crossbar arrays not only store input weights, but are also used to perform dot-product operations in an analog manner. While the use of crossbar memory as an analog dot-product engine is well known, no prior work has designed or characterized a full-fledged accelerator based on crossbars. In particular, our work makes the following contributions: (i) We design a pipelined architecture, with some crossbars dedicated for each neural network layer, and eDRAM buffers that aggregate data between pipeline stages. (ii) We define new data encoding techniques that are amenable to analog computations and that can reduce the high overheads of analog-to-digital conversion (ADC). (iii) We define the many supporting digital components required in an analog CNN accelerator and carry out a design space exploration to identify the best balance of memristor storage/compute, ADCs, and eDRAM storage on a chip. On a suite of CNN and DNN workloads, the proposed ISAAC architecture yields improvements of 14.8×, 5.5×, and 7.5× in throughput, energy, and computational density (respectively), relative to the state-of-the-art DaDianNao architecture.

CACTI 6.0: A Tool to Model Large Caches

Abstract: © CACTI 6.0: A Tool to Model Large Caches Naveen Muralimanohar, Rajeev Balasubramonian, Norman P. Jouppi HP Laboratories HPL-2009-85 No keywords available. Future processors will likely have large on-chip caches with a possibility of dedicating an entire die for on-chip storage in a 3D stacked design. With the ever growing disparity between transistor and wire delay, the properties of such large caches will primarily depend on the characteristics of the interconnection networks that connect various sub-modules of a cache. CACTI 6.0 is a significantly enhanced version of the tool that primarily focuses on interconnect design for large caches. In addition to strengthening the existing analytical model of the tool for dominant cache components, CACTI 6.0 includes two major extensions over earlier versions: first, the ability to model Non-Uniform Cache Access (NUCA), and second, the ability to model different types of wires, such as RC based wires with different power, delay, and area characteristics and differential low-swing buses. This report details the analytical model assumed for the newly added modules along with their validation analysis. External Posting Date: April 21, 2009 [Fulltext] Approved for External Publication Internal Posting Date: April 21, 2009 [Fulltext] Published in International Symposium on Microarchitecture, Chicago, Dec 2007. Copyright International Symposium on Microarchitecture, 2007. CACTI 6.0: A Tool to Model Large Caches Naveen Muralimanohar, Rajeev Balasubramonian, Norman P. Jouppi † School of Computing, University of Utah ‡ Hewlett-Packard Laboratories Abstract Future processors will likely have large on-chip caches with a possibility of dedicating an entire die for on-chip storage in a 3D stacked design. With the ever growing disparity between transistor and wire delay, the properties of such large caches will primarily depend on the characteristics of the interconnection networks that connect various sub-modules of a cache. CACTI 6.0 is a significantly enhanced version of the tool that primarily focuses on interconnect design for large caches. In addition to strengthening the existing analytical model of the tool for dominant cache components, CACTI 6.0 includes two major extensions over earlier versions: first, the ability to model Non-Uniform Cache Access (NUCA), and second, the ability to model different types of wires, such as RC based wires with different power, delay, and area characteristics and differential low-swing buses. This report details the analytical model assumed for the newly added modules along with their validation analysis.Future processors will likely have large on-chip caches with a possibility of dedicating an entire die for on-chip storage in a 3D stacked design. With the ever growing disparity between transistor and wire delay, the properties of such large caches will primarily depend on the characteristics of the interconnection networks that connect various sub-modules of a cache. CACTI 6.0 is a significantly enhanced version of the tool that primarily focuses on interconnect design for large caches. In addition to strengthening the existing analytical model of the tool for dominant cache components, CACTI 6.0 includes two major extensions over earlier versions: first, the ability to model Non-Uniform Cache Access (NUCA), and second, the ability to model different types of wires, such as RC based wires with different power, delay, and area characteristics and differential low-swing buses. This report details the analytical model assumed for the newly added modules along with their validation analysis.

Optimizing NUCA Organizations and Wiring Alternatives for Large Caches with CACTI 6.0

Abstract: A significant part of future microprocessor real estate will be dedicated to L2 or L3 caches. These on-chip caches will heavily impact processor perfor- mance, power dissipation, and thermal management strategies. There are a number of interconnect design considerations that influence power/performance/area characteristics of large caches, such as wire mod- els (width/spacing/repeaters), signaling strategy (RC/differential/transmission), router design, etc. Yet, to date, there exists no analytical tool that takes all of these parameters into account to carry out a design space exploration for large caches and estimate an optimal organization. In this work, we implement two major extensions to the CACTI cache modeling tool that focus on interconnect design for a large cache. First, we add the ability to model different types of wires, such as RC-based wires with different power/delay characteristics and differential low-swing buses. Second, we add the ability to model Non-uniform Cache Access (NUCA). We not only adopt state-of-the-art design space exploration strategies for NUCA, we also enhance this exploration by considering on-chip network contention and a wider spectrum of wiring and routing choices. We present a validation analysis of the new tool (to be released as CACTI 6.0) and present a case study to showcase how the tool can improve architecture research methodologies. Keywords: cache models, non-uniform cache archi- tectures (NUCA), memory hierarchies, on-chip intercon- nects.

Energy-efficient processor design using multiple clock domains with dynamic voltage and frequency scaling

Abstract: As clock frequency increases and feature size decreases, clock distribution and wire delays present a growing challenge to the designers of singly-clocked, globally synchronous systems. We describe an alternative approach, which we call a multiple clock domain (MCD) processor, in which the chip is divided into several clock domains, within which independent voltage and frequency scaling can be performed. Boundaries between domains are chosen to exploit existing queues, thereby minimizing inter-domain synchronization costs. We propose four clock domains, corresponding to the front end , integer units, floating point units, and load-store units. We evaluate this design using a simulation infrastructure based on SimpleScalar and Wattch. In an attempt to quantify potential energy savings independent of any particular on-line control strategy, we use off-line analysis of traces from a single-speed run of each of our benchmark applications to identify profitable reconfiguration points for a subsequent dynamic scaling run. Using applications from the MediaBench, Olden, and SPEC2000 benchmark suites, we obtain an average energy-delay product improvement of 20% with MCD compared to a modest 3% savings from voltage scaling a single clock and voltage system.

Memory hierarchy reconfiguration for energy and performance in general-purpose processor architectures

Abstract: Conventional microarchitectures choose a single memory hierarchy design point targeted at the average application. In this paper, we propose a cache and TLB layout and design that leverages repeater insertion to provide dynamic low-cost configurability trading off size and speed on a per application phase basis. A novel configuration management algorithm dynamically detects phase changes and reacts to an application's hit and miss intolerance in order to improve memory hierarchy performance while taking energy consumption into consideration. When applied to a two-level cache and TLB hierarchy at 0.1 /spl mu/m technology, the result is an average 15% reduction in cycles per instruction (CPI), corresponding to an average 27% reduction in memory-CPI, across a broad class of applications compared to the best conventional two-level hierarchy of comparable size. Projecting to sub-.1 /spl mu/m technology design considerations that call for a three-level conventional cache hierarchy for performance reasons, we demonstrate that a configurable L2/L3 cache hierarchy coupled with a conventional LI results in an average 43% reduction in memory hierarchy energy in addition to improved performance.

In-Datacenter Performance Analysis of a Tensor Processing Unit

Abstract: Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.

In-Datacenter Performance Analysis of a Tensor Processing Unit

Abstract: Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU) --- deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X -- 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X -- 80X higher. Moreover, using the CPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.

EIE: efficient inference engine on compressed deep neural network

Abstract: State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power.Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and having multiple connections share the same weight. We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. Going from DRAM to SRAM gives EIE 120× energy saving; Exploiting sparsity saves 10×; Weight sharing gives 8×; Skipping zero activations from ReLU saves another 3×. Evaluated on nine DNN benchmarks, EIE is 189× and 13× faster when compared to CPU and GPU implementations of the same DNN without compression. EIE has a processing power of 102 GOPS working directly on a compressed network, corresponding to 3 TOPS on an uncompressed network, and processes FC layers of AlexNet at 1.88×104 frames/sec with a power dissipation of only 600mW. It is 24,000× and 3,400× more energy efficient than a CPU and GPU respectively. Compared with DaDianNao, EIE has 2.9×, 19× and 3× better throughput, energy efficiency and area efficiency.

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

Abstract: Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems. This article aims to provide a comprehensive tutorial and survey about the recent advances toward the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic codesigns, being proposed in academia and industry. The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the tradeoffs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.

Deep learning with coherent nanophotonic circuits

Abstract: Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.