Implementation of Input Oriented Dynamic Voltage and Frequency Scaling for Multiplier on FPGA

This paper presents an Implementation of Dynamic voltage and frequency scaling according to input data. In the conventional method the power supply is fixed and independent on workload, so, voltage and area will be consumed unnecessary .Paper proposes the approach which focuses on making system dynamic for low power digital multiplier on reconfigurable device FPGA (Spartan III). For making system Dynamic input workload should be known and scanning is used to detect range of input so system can adjust voltage and frequency. Control signal generated from scanning which can dynamically change voltage and frequency for low power consumption according to input data

Energy-precision tradeoffs in the graphics pipeline

The energy consumption of a graphics processing unit (GPU) is an important factor in its design, whether for a server, desktop, or mobile device. Mobile products, such as smart phones, tablets, and laptop computers, rely on batteries to function; the less the demand for power is on these batteries, the longer they will last before needing to be recharged. GPUs used in servers and desktops, while not dependent on a battery for operation, are still limited by the efficiency of power supplies and heat dissipation techniques. In this dissertation, I propose to lower the energy consumption of GPUs by reducing the precision of floating-point arithmetic in the graphics pipeline and the data sent and stored on- and off-chip. The key idea behind this work is twofold: energy can be saved through a systematic and targeted reduction in the number of bits 1) computed and 2) communicated. Reducing the number of bits computed will necessarily reduce either the precision or range of a floating point number. I focus on saving energy by way of reducing precision, which can exploit the over-provisioning of bits in many stages of the graphics pipeline. Reducing the number of bits communicated takes several forms. First, I propose enhancements to existing compression schemes for off-chip buffers to save bandwidth. I also suggest a simple extension that exploits unused bits in reduced-precision data undergoing compression. Finally, I present techniques for saving energy in on-chip communication of reduced-precision data. By designing and simulating variable-precision arithmetic circuits with promising energy versus precision characteristics and tradeoffs, I have developed an energy model for GPUs. Using this model and my techniques, I have shown that significant savings (up to 70% in computation in the vertex and pixel shader stages) are possible by reducing the precision of the arithmetic. Further, my compression approaches have enabled improvements of 1.26x over past work, and a general-purpose compressor design has achieved bandwidth savings of 34%, 87%, and 65% for color, depth, and geometry data, respectively, which is competitive with past work. Lastly, an initial exploration in signal gating unused lines in on-chip buses has suggested savings of 13-48% for the tested applications' traffic from a multiprocessor's register file to its L1 cache

The Design Space of Ultra-low Energy Asymmetric Cryptography

The energy cost of asymmetric cryptography, a vital component of modern secure communications, inhibits its wide spread adoption within the ultra-low energy regimes such as Implantable Medical Devices (IMDs), Wireless Sensor Networks (WSNs), and Radio Frequency Identification tags (RFIDs). In literature, a plethora of hardware and software acceleration techniques exists for improving the performance of asymmetric cryptography. However, very little attention has been focused on the energy efficiency. Therefore, in this dissertation, I explore the design space thoroughly, evaluating proposed hardware acceleration techniques in terms of energy cost and showing how effective they are at reducing the energy per cryptographic operation. To do so, I estimate the energy consumption for six different hardware/software configurations across five levels of security, including both GF(p) and GF(2^m) computation. First, we design and evaluate an efficient baseline architecture for pure software-based cryptography, which is centered around a pipelined RISC processor with 256KB of program ROM and 16KB of RAM. Then, we augment our processor design with simple, yet beneficial instruction set extensions for GF(p) computation and evaluate the improvement in terms of energy per cryptographic operation compared to the baseline microarchitecture. While examining the energy breakdown of the system, it became clear that fetching instructions from program memory was contributing significantly to the overall energy consumption. Thus, we implement a parameterizable instruction cache and simulate various configurations. We determine that for our working set, the energy-optimal instruction cache is 4KB, providing a 25% energy improvement over the baseline architecture for a 192-bit key-size. Next, we introduce a reconfigurable GF(p) accelerator to our microarchitecture and mea sure the energy per operation against the baseline and the ISA extensions. For ISA extensions, we show between 1.32 and 1.45 factor improvement in energy efficiency over baseline, while for full acceleration we demonstrate a 5.17 to 6.34 factor improvement. Continuing towards greater efficiency, we investigate the energy efficiency of different arithmetic by first adding GF(2^m) instruction set extensions to our processor architecture and comparing them to their GF(p) counterpart. Finally, we design a non-configurable 163-bit GF(2^m) accelerator and perform some initial energy estimates, comparing them with our prior work. In the end, we discuss our ongoing research and make suggestions for future work. The work presented here, along with proposed future work, will aid in bringing asymmetric cryptography within reach of ultra-low energy devices

A Construction Kit for Efficient Low Power Neural Network Accelerator Designs

Implementing embedded neural network processing at the edge requires efficient hardware acceleration that couples high computational performance with low power consumption. Driven by the rapid evolution of network architectures and their algorithmic features, accelerator designs are constantly updated and improved. To evaluate and compare hardware design choices, designers can refer to a myriad of accelerator implementations in the literature. Surveys provide an overview of these works but are often limited to system-level and benchmark-specific performance metrics, making it difficult to quantitatively compare the individual effect of each utilized optimization technique. This complicates the evaluation of optimizations for new accelerator designs, slowing-down the research progress. This work provides a survey of neural network accelerator optimization approaches that have been used in recent works and reports their individual effects on edge processing performance. It presents the list of optimizations and their quantitative effects as a construction kit, allowing to assess the design choices for each building block separately. Reported optimizations range from up to 10'000x memory savings to 33x energy reductions, providing chip designers an overview of design choices for implementing efficient low power neural network accelerators

An investigation into adaptive power reduction techniques for neural hardware

In light of the growing applicability of Artificial Neural Network (ANN) in the signal processing field [1] and the present thrust of the semiconductor industry towards lowpower SOCs for mobile devices [2], the power consumption of ANN hardware has become a very important implementation issue. Adaptability is a powerful and useful feature of neural networks. All current approaches for low-power ANN hardware techniques are ‘non-adaptive’ with respect to the power consumption of the network (i.e. power-reduction is not an objective of the adaptation/learning process). In the research work presented in this thesis, investigations on possible adaptive power reduction techniques have been carried out, which attempt to exploit the adaptability of neural networks in order to reduce the power consumption. Three separate approaches for such adaptive power reduction are proposed: adaptation of size, adaptation of network weights and adaptation of calculation precision. Initial case studies exhibit promising results with significantpower reduction

Algorithm and Hardware Co-Design for Local/Edge Computing

Advances in VLSI manufacturing and design technology over the decades have created many computing paradigms for disparate computing needs. With concerns for transmission cost, security, latency of centralized computing, edge/local computing are increasingly prevalent in the faster growing sectors like Internet-of-Things (IoT) and other sectors that require energy/connectivity autonomous systems such as biomedical and industrial applications. Energy and power efficient are the main design constraints in local and edge computing. While there exists a wide range of low power design techniques, they are often underutilized in custom circuit designs as the algorithms are developed independent of the hardware. Such compartmentalized design approach fails to take advantage of the many compatible algorithmic and hardware techniques that can improve the efficiency of the entire system. Algorithm hardware co-design is to explore the design space with whole stack awareness. The main goal of the algorithm hardware co-design methodology is the enablement and improvement of small form factor edge and local VLSI systems operating under strict constraints of area and energy efficiency. This thesis presents selected works of application specific digital and mixed-signal integrated circuit designs. The application space ranges from implantable biomedical devices to edge machine learning acceleration