Download the latest versions of Achronix application notes, datasheets, product briefs, user guides and white papers.

Select the individual tabs below to browse through each type of documentation. Or use the filter to only see documentation related to your product of interest.

Some documents are restricted (denoted by the lock symbol in the download button) and require a support portal account to access the download. To download a restricted document, enter your support portal account credentials when prompted. Don't have a support portal account? Register for an account here: Achronix Support Account Registration

Title Description Version Released Date Document File
How to Meet Power Performance and Cost for Autonomous Vehicle Systems using Speedcore eFPGAs (WP015)

In the advanced, fully autonomous, self-driving vehicles of the future, the existence of dozens and even hundreds of distributed CPUs and numerous other processing elements is assured. Peripheral sensor-fusion and other processing tasks can be served by ASICs, SoCs, or traditional FPGAs. But the introduction of embedded FPGA blocks such as Achronix's Speedcore eFPGA IP provides numerous system-design advantages in terms of shorter latency, more security, greater bandwidth, and better reliability that are simply not possible when using CPUs, GPUs, or even standalone FPGAs.

1.0 Download
Achieving ASIC Timing Closure with Speedcore eFPGAs (WP013)

Achronix's Speedcore eFPGA IP allows companies to embed a programmable logic fabric in their ASICs, delivering to end users the capability to modify or upgrade the functionality of an ASIC after being deployed in the field. This flexibility dramatically expands the solution space that can be served by the ASIC as it can be updated to support changing standards and algorithms. Timing closure is particularly challenging due to the fact that the eFPGA fabric may host any number of designs over the course of device operation. Each of those designs must work independently with the rest of the ASIC, and timing closure can only be said to have been met if all of the possible designs targeting the eFPGA fabric can meet timing.

1.0 Download
2018 Ushers in a Renewed Push to the Edge (WP012)

The past decade has seen massive growth in centralized computing, with data processing flowing to the cloud to take advantage of low-cost dedicated data centers. It was a trend that seemed at odds with the general trend in computing — a trend that started with the mainframe but moved progressively towards ambient intelligence and the internet of things (IoT). As we move into 2018, this centralization is reaching its limit. The volume of data that will be needed to drive the next wave of applications is beginning to force a change in direction.

1.0 Download
The Ideal Solution for AI Applications — Speedcore eFPGAs (WP011)

AI requires a careful balance of datapath performance, memory latency, and throughput that requires an approach based on pulling as much of the functionality as possible into an ASIC or SoC. But that single-chip device needs plasticity to be able to handle the changes in structure that are inevitable in machine-learning projects. Adding eFPGA technology provides the mixture of flexibility and support for custom logic that the market requires. Achronix provides not only the building blocks required for an AI-ready eFPGA solution, but also delivers a framework that supports design through to debug and test of the final application. Only Achronix Speedcore IP has the right mix of features for advanced AI that will support a new generation of real-time, self-learning systems.

1.0 Download
Enhancing eFPGA Functionality with Speedcore Custom Blocks (WP009)

Achronix Speedcore™ eFPGA IP can be integrated in an SoC for high-performance, compute-intensive and realtime processing applications such as AI, automotive sensor fusion, network acceleration and wireless 5G. Speedcore eFPGA IP is a game-changer for SoC developers, allowing them to add flexibility to their products by including FPGA technology in their ASICs. For SoC development, companies specify the quantity and mix of lookup-table (LUT) logic, embedded memory blocks, and DSP blocks that best meets their needs. Along with these functions, Achronix now offers the ability for companies to define custom block functions, optimized for their application, that can also be included in the eFPGA fabric. Speedcore custom blocks increase die area efficiency, increase performance and lower power.

1.0 Download
Title Description Version Released Date Document File
ACE ECO Flow Guide (AN015)

This tutorial serves as an introduce to the ACE engineering change order (ECO) suite — a set of Tcl commands that can add or remove instances, nets, pin connections, and more from a placed-and-routed design.

1.0 ACE_ECO_Flow_Guide_AN015.pdf
Pipelining the CPU Interface (AN016)

A Speedcore instance hosted in an SoC supports three different configuration modes: CPU, serial flash and JTAG. In CPU mode, an external CPU acts as the master and controls the programming operations for the Speedcore eFPGA, and offers a high-speed method for loading configuration data.

1.0 Pipelining_the_CPU_Interface_AN016.pdf
Repeatability in ACE (AN012)

One of the desired requirements of any FPGA design tool is the ability to reproduce the exact same results every time the tool is run under the same conditions — a requirement refereed to as repeatability. The ACE placer and router are deterministic, delivering 100% repeatability.

1.2 Repeatability_in_ACE_AN012.pdf
Clock Design Planning for Speedcore eFPGAs (AN011)

Speedcore eFPGAs have a robust clocking architecture. While some designs only use a single main clock, others can have complicated clocking schemes. It is important for designers to understand the different types of clocks available in the Speedcore architecture, and how to get the best design out of the clocking resources available.

1.0 Clock_Design_Planning_for_Speedcore_eFPGAs_AN011.pdf
Coding Guidelines for Speedcore eFPGAs (AN003)

In order to obtain the best quality of results (QoR) when targeting any design to an FPGA, it is sometimes necessary to structure the RTL and constraints to take best advantage of the underlying FPGA architecture and the built-in features of the tool chain.

2.0 Coding_Guidelines_for_Speedcore_eFPGAs_AN003.pdf
Title Description Version Released Date Document File
Speedster7t Power Estimator User Guide (UG093)

The Achronix Speedster7t Power Estimator tool provides a platform to calculate the power requirements for the Achronix 7nm standalone FPGAs. This user guide gives a detailed overview of the thermal and power needs depending on the device, environment and utilization of components in the design. The power estimator tool can be used at any stage of the design process to obtain an estimate of the total power dissipation from the device. This estimate could then be compared with post-implementation results using the ACE-generated power report.

1.0 Download
Simulation User Guide (UG072)

The Achronix tool suite includes synthesis and place-and-route software that maps RTL designs (VHDL or Verilog) into Achronix devices. In addition to synthesis and place-and-route functions, the Achronix software tools flow also supports simulation at several flow steps (RTL, Synthesized Netlist, and Post Place-And-Routed Netlist). This guide covers the simulation flow for Achronix devices.

1.5 Download
Speedster7t DDR User Guide (UG096)

The Achronix Speedster7t FPGA family provides DDR subsystems that enable the user to fully utilize the low latency and high-bandwidth efficiency of these interfaces for critical applications such as high-performance compute and machine learning systems. The DDR subsystem supports memory devices and features compliant with JEDEC Standard JESD79-4B.

1.0 Download
Speedster7t Configuration User Guide (UG094)

At startup, Speedster7t FPGAs require configuration by the end user via a bitstream. This bitstream can be programmed through one of four available interfaces in the FPGA configuration unit (FCU), which is logic that controls the configuration process of the Speedster7t FPGA.

0.1 Download
Speedster7t Machine Learning Processing User Guide (UG088)

The machine learning processing block (MLP) is an array of up to 32 multipliers, followed by an adder tree, an accumulator, and a rounding/saturation/normalize block.The MLP also includes two memory blocks, a BRAM72k and LRAM2k, that can be used individually or in conjunction with the array of multipliers. The number of multipliers available varies with the bit width of each operand and the total width of input data. When the MLP is used in conjunction with a BRAM72k, the amount of data inputs to the MLP block increases along with the number of multipliers available. 

0.9 Download