Tutorial: Floating-point arithmetic on FPGAs
This article explains the basics of floating-point arithmetic, how floating-point units (FPUs) work, and how to use FPGAs for easy, low-cost floating-point processing.
Vendor:
Xilinx, Inc.
Category:
Arithmetic Units
Multiplier
The multiplier operation is essential and abundant in DSP Applications.
Overview
The multiplier operation is essential and abundant in DSP Applications. Achieving maximum implementation efficiency and clock performance is therefore critical to DSP systems and frequently presents a significant challenge to hardware engineers.
The Multiplier LogiCORE™ simplifies this challenge by abstracting away FPGA device specifics, while maintaining the required maximum performance and resource efficiency. The multiplier is able to generate parallel multipliers, and constant coefficient multipliers, both with differing implementation styles. Furthermore, with the aid of instantaneous resource estimation, hardware engineers can rapidly select the optimal solution for their system.
This IP provides fine control over the latency (pipelining) of the multipliers (purely combinatorial to fully pipelined) and symmetric rounding implemented in the DSP48 slice. Fully pipelined implementations enable maximum clock frequency performance
The Multiplier LogiCORE™ simplifies this challenge by abstracting away FPGA device specifics, while maintaining the required maximum performance and resource efficiency. The multiplier is able to generate parallel multipliers, and constant coefficient multipliers, both with differing implementation styles. Furthermore, with the aid of instantaneous resource estimation, hardware engineers can rapidly select the optimal solution for their system.
This IP provides fine control over the latency (pipelining) of the multipliers (purely combinatorial to fully pipelined) and symmetric rounding implemented in the DSP48 slice. Fully pipelined implementations enable maximum clock frequency performance
Key features
- 2's complement signed/unsigned fixed point multiplier
- Parallel and fixed constant coefficient multipliers
- Supports Inputs ranging 1 to 64 bits wide and outputs ranging from 1 to 128 bits wide with any portion of the full product selectable
- Supports symmetric rounding to infinity when using the DSP Slice
- Instantaneous Resource Estimation
- Optional Clock Enable, and Synchronous Clear
- VHDL behavioral models
- Instantaneous Resource Estimation
- For use with Vivado® IP Catalog and Xilinx System Generator for DSP
Specifications
Identity
Part Number
Multiplier
Vendor
Xilinx, Inc.
Type
Silicon IP
Files
Note: some files may require an NDA depending on provider policy.
Provider
HQ:
USA
Learn more about Arithmetic Units IP core
Implementing matrix inversions in fixed-point hardware
We implement fixed-point matrix inversion on a Virtex-4 FPGA using a synthesizable QR-decomposition MATLAB model and the AccelDSP Synthesis tool. The resulting function occupies 12% of a XC4VSX55 device and has a 1.7 MSPS data rate.
A Comprehensive Post-Quantum Cryptography (PQC) Solution based on Physical Unclonable Function (PUF)
This article provides an in-depth analysis of the specific PPA challenges introduced by PQC and elucidates how PUF-PQC leverages its unique dualtrack strategy to deliver a robust and flexible Hardware Root of Trust (HRoT) across diverse application scenarios. Furthermore, it demonstrates the integration of Physical Unclonable Function (PUF) with a NIST SP 800-90B compliant True Random Number Generator (TRNG) to serve as critical components of PUFrt (Root of Trust), ensuring the security of post-quantum key generation starting from the entropy source.
ML-KEM explained: Quantum-safe Key Exchange for secure embedded Hardware
KiviCore´s blog post discusses Quantum-safe key exchange with NIST FIPS 203 ML-KEM and how IP cores simplify PQC integration on FPGA and SoCs.
Accelerating RTL Design with Agentic AI: A Multi-Agent LLM-Driven Approach
In today’s fast-paced silicon industry, hardware design is under constant pressure to innovate, iterate, and ship faster. Traditional Register Transfer Level (RTL) design processes—though foundational—are time-consuming, error-prone, and heavily reliant on manual expertise. With growing design complexity and shrinking time-to-market, the need for automation in digital design is more critical than ever.
LTE Single Carrier DFT: Faster Circuits with Reduced FPGA LUT/Register Usage
Given the prominence of the LTE protocol in wireless devices, it is surprising that there are very few DFT FPGA circuit implementations from which to choose. This is likely due to the complexity of the circuit, which must accommodate run-time choice of many and large non-power-of-two transforms, requiring multiple radices for efficient DFT calculation.
Frequently asked questions about Arithmetic Unit IP cores
What is Multiplier?
Multiplier is a Arithmetic Units IP core from Xilinx, Inc. listed on Semi IP Hub.
How should engineers evaluate this Arithmetic Units?
Engineers should review the overview, key features, supported foundries and nodes, maturity, deliverables, and provider information before shortlisting this Arithmetic Units IP.
Can this semiconductor IP be compared with similar products?
Yes. Buyers can compare this product with similar semiconductor IP cores or IP families based on category, provider, process options, and structured technical specifications.