OPTIMIZING LARGE COMBINATIONAL NETWORKS FOR K-LUT BASED FPGA MAPPING

Optimizing by partitioning is a central problem in VLSI design automation, addressing circuit’s manufacturability. Circuit partitioning has multiple applications in VLSI design. One of the most common is that of dividing combinational circuits (usually large ones) that will not fit on a single package among a number of packages. Partitioning is of practical importance for k-LUT based FPGA circuit implementation. In this work is presented multilevel a multi-resource partitioning algorithm for partitioning large combinational circuits in order to efficiently use existing and commercially available FPGAs packagestwo-way partitioning, multi-way partitioning, recursive partitioning, flat partitioning, critical path, cutting cones, bottom-up clusters, top-down min-cut

Placement-Driven Technology Mapping for LUT-Based FPGAs

In this paper, we study the problem of placement-driven technology mapping for table-lookup based FPGA architectures to optimize circuit performance. Early work on technology mapping for FPGAs such as Chortle-d[14] and Flowmap[3] aim to optimize the depth of the mapped solution without consideration of interconnect delay. Later works such as Flowmap-d[7], Bias-Clus[4] and EdgeMap consider interconnect delays during mapping, but do not take into consideration the effects of their mapping solution on the final placement. Our work focuses on the interaction between the mapping and placement stages. First, the interconnect delay information is estimated from the placement, and used during the labeling process. A placement-based mapping solution which considers both global cell congestion and local cell congestion is then developed. Finally, a legalization step and detailed placement is performed to realize the design. We have implemented our algorithm in a LUT based FPGA technology mapping package named PDM (Placement-Driven Mapping) and tested the implementation on a set of MCNC benchmarks. We use the tool VPR[1][2] for placement and routing of the mapped netlist. Experimental results show the longest path delay on a set of large MCNC benchmarks decreased by 12.3 % on the average

A Novel Basis for Logic Rewriting

Given a set of logic primitives and a Boolean function, exact synthesis finds the optimum representation (e.g., depth or size) of the function in terms of the primitives. Due to its high computational complexity, the use of exact synthesis is limited to small networks. Some logic rewriting algorithms use exact synthesis to replace small subnetworks by their optimum representations. However, conventional approaches have two major drawbacks. First, their scalability is limited, as Boolean functions are enumerated to precompute their optimum representations. Second, the strategies used to replace subnetworks are not satisfactory. We show how the use of exact synthesis for logic rewriting can be improved. To this end, we propose a novel method that includes various improvements over conventional approaches: (i) we improve the subnetwork selection strategy, (ii) we show how enumeration can be avoided, allowing our method to scale to larger subnetworks, and (iii) we introduce XOR Majority Graphs (XMGs) as compact logic representations that make exact synthesis more efficient. We show a 45.8% geometric mean reduction (taken over size, depth, and switching activity), a 6.5% size reduction, and depth · size reductions of 8.6%, compared to the academic state-of-the-art. Finally, we outperform 3 over 9 of the best known size results for the EPFL benchmark suite, reducing size by up to 11.5% and depth up to 46.7%

Cost Effective Implementation of Fixed Point Adders for LUT based FPGAs using Technology Dependent Optimizations

Modern day field programmable gate arrays (FPGAs) have very huge and versatile logic resources resulting in the migration of their application domain from prototype designing to low and medium volume production designing. Unfortunately most of the work pertaining to FPGA implementations does not focus on the technology dependent optimizations that can implement a desired functionality with reduced cost. In this paper we consider the mapping of simple ripple carry fixed-point adders (RCA) on look-up table (LUT) based FPGAs. The objective is to transform the given RCA Boolean network into an optimized circuit netlist that can implement the desired functionality with minimum cost. We particularly focus on 6-input LUTs that are inherent in all the modern day FPGAs. Technology dependent optimizations are carried out to utilize this FPGA primitive efficiently and the result is compared against various adder designs. The implementation targets the XC5VLX30-3FF324 device from Xilinx Virtex-5 FPGA family. The cost of the circuit is expressed in terms of the resources utilized, critical path delay and the amount of on-chip power dissipated. Our implementation results show a reduction in resources usage by at least 50%; increase in speed by at least 10% and reduction in dynamic power dissipation by at least 30%. All this is achieved without any technology independent (architectural) modification

LUT Mapping and Optimization for Majority-Inverter Graphs

A Majority-Inverter Graph (MIG) is a directed acyclic graph in which every vertex represents a three-input majority operation and edges may be complemented to indicate operand inversion. MIGs have algebraic and Boolean properties that enable efficient logic optimization. They have been shown to obtain superior synthesis results as compared to state-of-the- art And-Inverter Graph (AIG) based algorithms. In this paper, we extend MIGs to Functionally Reduced MIGs (FRMIGs), analogous to the extension of AIGs to Functionally Reduced AIGs (FRAIGs). This enables the use of MIGs in a lossless synthesis design flow. We present an FRMIG based technology mapper for lookup tables (LUTs). Any MIG may be mapped to a k- LUT network. Using exact synthesis we may decompose the k- LUT network back into an equivalent MIG. We show how LUT mapping and exact k-LUT decomposition can be used to create an MIG optimization method. Finally, we present the results of applying our new optimization method and LUT mapper to both logic optimization and technology mapping

Towards Machine Learning-Based FPGA Backend Flow: Challenges and Opportunities

Field-Programmable Gate Array (FPGA) is at the core of System on Chip (SoC) design across various Industry 5.0 digital systems—healthcare devices, farming equipment, autonomous vehicles and aerospace gear to name a few. Given that pre-silicon verification using Computer Aided Design (CAD) accounts for about 70% of the time and money spent on the design of modern digital systems, this paper summarizes the machine learning (ML)-oriented efforts in different FPGA CAD design steps. With the recent breakthrough of machine learning, FPGA CAD tasks—high-level synthesis (HLS), logic synthesis, placement and routing—are seeing a renewed interest in their respective decision-making steps. We focus on machine learning-based CAD tasks to suggest some pertinent research areas requiring more focus in CAD design. The development of open-source benchmarks optimized for an end-to-end machine learning experience, intra-FPGA optimization, domain-specific accelerators, lack of explainability and federated learning are the issues reviewed to identify important research spots requiring significant focus. The potential of the new cloud-based architectures to understand the application of the right ML algorithms in FPGA CAD decision-making steps is discussed, together with visualizing the scenario of incorporating more intelligence in the cloud platform, with the help of relatively newer technologies such as CAD as Adaptive OpenPlatform Service (CAOS). Altogether, this research explores several research opportunities linked with modern FPGA CAD flow design, which will serve as a single point of reference for modern FPGA CAD flow design