Design of approximate overclocked datapath

Embedded applications can often demand stringent latency requirements. While high degrees of parallelism within custom FPGA-based accelerators may help to some extent, it may also be necessary to limit the precision used in the datapath to boost the operating frequency of the implementation. However, by reducing the precision, the engineer introduces quantisation error into the design. In this thesis, we describe an alternative circuit design methodology when considering trade-offs between accuracy, performance and silicon area. We compare two different approaches that could trade accuracy for performance. One is the traditional approach where the precision used in the datapath is limited to meet a target latency. The other is a proposed new approach which simply allows the datapath to operate without timing closure. We demonstrate analytically and experimentally that for many applications it would be preferable to simply overclock the design and accept that timing violations may arise. Since the errors introduced by timing violations occur rarely, they will cause less noise than quantisation errors. Furthermore, we show that conventional forms of computer arithmetic do not fail gracefully when pushed beyond the deterministic clocking region. In this thesis we take a fresh look at Online Arithmetic, originally proposed for digit serial operation, and synthesize unrolled digit parallel online arithmetic operators to allow for graceful degradation. We quantify the impact of timing violations on key arithmetic primitives, and show that substantial performance benefits can be obtained in comparison to binary arithmetic. Since timing errors are caused by long carry chains, these result in errors in least significant digits with online arithmetic, causing less impact than conventional implementations.Open Acces

LUT Based Generalized Parallel Counters for State-of-art FPGAs

Generalized Parallel Counters (GPCs) are frequently used in constructing high speed compressor trees. Previous work has focused on achieving efficient mapping of GPCs on FPGAs by using a combination of general Look-up table (LUT) fabric and specialized fast carry chains. The resulting structures are purely combinational and cannot be efficiently pipelined to achieve the potential FPGA performance. In this paper, we take an alternate approach and try to eliminate the fast carry chain from the GPC structure. We present a heuristic that maps GPCs on FPGAS using only general LUT fabric. The resultant GPCs are then easily re-timed by placing registers at the fan-out nodes of each LUT. We have used our heuristic on various GPCs reported in prior work. Our heuristic successfully eliminates the carry chain from the GPC structure with the same LUT count in most of the cases. Experimental results using Xilinx Kintex-7 FPGAs show a considerable reduction in critical path and dynamic power dissipation with same area utilization in most of the cases

Input-Conscious Approximate Multiply-Accumulate (MAC) Unit for Energy-Efficiency

The Multiply-Accumulate Unit (MAC) is an integral computational component of all digital signal processing (DSP) architectures and thus has a significant impact on their speed and power dissipation. Due to an extraordinary explosion in the number of battery-powered “Internet of Things” (IoT) devices, the need for reducing the power consumption of DSP architectures has tremendously increased. Approximate computing (AxC) has been proposed as a potential solution for this problem targeting error-resilient applications. In this paper, we present a novel FPGA implementation for input-aware energy-efficient 8-bit approximate MAC (AxMAC) unit that reduces its power consumption by: performing multiplication operation approximately, or approximating the input operands then replacing multiplication by a simple shift operation. We propose an input-aware conditional block to bypass operands multiplication by (1) zero forwarding for zero-value operands, (2) judiciously approximating 43.8% of inputs into power-of-2 values, and (3) replacing the multiplication of power-of-2 operands by a simple shift operation. Experimental results show that these simplification techniques reduce delay, power and energy consumption with an acceptable quality degradation. We evaluate the effectiveness of the proposed AxMAC units on two image processing applications, i.e., image blending and filtering, and a logistic regression classification application. These applications demonstrate a negligible quality loss, with 66.6% energy reduction and 5% area overhead

Closing the Gap between FPGA and ASIC:Balancing Flexibility and Efficiency

Despite many advantages of Field-Programmable Gate Arrays (FPGAs), they fail to take over the IC design market from Application-Specific Integrated Circuits (ASICs) for high-volume and even medium-volume applications, as FPGAs come with significant cost in area, delay, and power consumption. There are two main reasons that FPGAs have huge efficiency gap with ASICs: (1) FPGAs are extremely flexible as they have fully programmable soft-logic blocks and routing networks, and (2) FPGAs have hard-logic blocks that are only usable by a subset of applications. In other words, current FPGAs have a heterogeneous structure comprised of the flexible soft-logic and the efficient hard-logic blocks that suffer from inefficiency and inflexibility, respectively. The inefficiency of the soft-logic is a challenge for any application that is mapped to FPGAs, and lack of flexibility in the hard-logic results in a waste of resources when an application cannot use the hard-logic. In this thesis, we approach the inefficiency problem of FPGAs by bridging the efficiency/flexibility gap of the hard- and soft-logic. The main goal of this thesis is to compromise on efficiency of the hard-logic for flexibility, on the one hand, and to compromise on flexibility of the soft-logic for efficiency, on the other hand. In other words, this thesis deals with two issues: (1) adding more generality to the hard-logic of FPGAs, and (2) improving the soft-logic by adapting it to the generic requirements of applications. In the first part of the thesis, we introduce new techniques that expand the functionality of FPGAs hard-logic. The hard-logic includes the dedicated resources that are tightly coupled with the soft-logic –i.e., adder circuitry and carry chains –as well as the stand-alone ones –i.e., DSP blocks. These specialized resources are intended to accelerate critical arithmetic operations that appear in the pre-synthesis representation of applications; we introduce mapping and architectural solutions, which enable both types of the hard-logic to support additional arithmetic operations. We first present a mapping technique that extends the application of FPGAs carry chains for carry-save arithmetic, and then to increase the generality of the hard-logic, we introduce novel architectures; using these architectures, more applications can take advantage of FPGAs hard-logic. In the second part of the thesis, we improve the efficiency of FPGAs soft-logic by exploiting the circuit patterns that emerge after logic synthesis, i.e., connection and logic patterns. Using these patterns, we design new soft-logic blocks that have less flexibility, but more efficiency than current ones. In this part, we first introduce logic chains, fixed connections that are integrated between the soft-logic blocks of FPGAs and are well-suited for long chains of logic that appear post-synthesis. Logic chains provide fast and low cost connectivity, increase the bandwidth of the logic blocks without changing their interface with the routing network, and improve the logic density of soft-logic blocks. In addition to logic chains and as a complementary contribution, we present a non-LUT soft-logic block that comprises simple and pre-connected cells. The structure of this logic block is inspired from the logic patterns that appear post-synthesis. This block has a complexity that is only linear in the number of inputs, it sports the potential for multiple independent outputs, and the delay is only logarithmic in the number of inputs. Although this new block is less flexible than a LUT, we show (1) that effective mapping algorithms exist, (2) that, due to their simplicity, poor utilization is less of an issue than with LUTs, and (3) that a few LUTs can still be used in extreme unfortunate cases. In summary, to bridge the gap between FPGAs and ASICs, we approach the problem from two complementary directions, which balance flexibility and efficiency of the logic blocks of FPGAs. However, we were able to explore a few design points in this thesis, and future work could focus on further exploration of the design space

Rethinking FPGA Architectures for Deep Neural Network applications

The prominence of machine learning-powered solutions instituted an unprecedented trend of integration into virtually all applications with a broad range of deployment constraints from tiny embedded systems to large-scale warehouse computing machines. While recent research confirms the edges of using contemporary FPGAs to deploy or accelerate machine learning applications, especially where the latency and energy consumption are strictly limited, their pre-machine learning optimised architectures remain a barrier to the overall efficiency and performance. Realizing this shortcoming, this thesis demonstrates an architectural study aiming at solutions that enable hidden potentials in the FPGA technology, primarily for machine learning algorithms. Particularly, it shows how slight alterations to the state-of-the-art architectures could significantly enhance the FPGAs toward becoming more machine learning-friendly while maintaining the near-promised performance for the rest of the applications. Eventually, it presents a novel systematic approach to deriving new block architectures guided by designing limitations and machine learning algorithm characteristics through benchmarking. First, through three modifications to Xilinx DSP48E2 blocks, an enhanced digital signal processing (DSP) block for important computations in embedded deep neural network (DNN) accelerators is described. Then, two tiers of modifications to FPGA logic cell architecture are explained that deliver a variety of performance and utilisation benefits with only minor area overheads. Eventually, with the goal of exploring this new design space in a methodical manner, a problem formulation involving computing nested loops over multiply-accumulate (MAC) operations is first proposed. A quantitative methodology for deriving efficient coarse-grained compute block architectures from benchmarks is then suggested together with a family of new embedded blocks, called MLBlocks

Variation-aware high-level DSP circuit design optimisation framework for FPGAs

The constant technology shrinking and the increasing demand for systems that operate under different power profiles with the maximum performance, have motivated the work in this thesis. Modern design tools that target FPGA devices take a conservative approach in the estimation of the maximum performance that can be achieved by a design when it is placed on a device, accounting for any variability in the fabrication process of the device. The work presented here takes a new view on the performance improvement of DSP designs by pushing them into the error-prone regime, as defined by the synthesis tools, and by investigating methodologies that reduce the impact of timing errors at the output of the system. In this work two novel error reduction techniques are proposed to address this problem. One is based on reduced-precision redundancy and the other on an error optimisation framework that uses information from a prior characterisation of the device. The first one is a generic architecture that is appended to existing arithmetic operators. The second defines the high-level parameters of the algorithm without using extra resources. Both of these methods allow to achieve graceful degradation whilst variation increases. A comparison of the new methods is laid against the existing methodologies, and conclusions drawn on the tradeoffs between their cost, in terms of resources and errors, and their benefits in terms of throughput. In some cases it is possible to double the performance of the design while still producing valid results.Open Acces

Approximation Opportunities in Edge Computing Hardware : A Systematic Literature Review

With the increasing popularity of the Internet of Things and massive Machine Type Communication technologies, the number of connected devices is rising. However, while enabling valuable effects to our lives, bandwidth and latency constraints challenge Cloud processing of their associated data amounts. A promising solution to these challenges is the combination of Edge and approximate computing techniques that allows for data processing nearer to the user. This paper aims to survey the potential benefits of these paradigms’ intersection. We provide a state-of-the-art review of circuit-level and architecture-level hardware techniques and popular applications. We also outline essential future research directions.publishedVersionPeer reviewe