Variable Precision DSP Blocks User Guide: Agilex™ 5 FPGAs and SoCs

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ID 813968
Date 9/20/2024
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Document Table of Contents
Document Table of Contents x
1. Agilex™ 5 Variable Precision DSP Blocks Overview 2. Agilex™ 5 Variable Precision DSP Blocks Architecture 3. Agilex™ 5 Variable Precision DSP Blocks Operational Modes 4. Agilex™ 5 Variable Precision DSP Blocks Design Considerations 5. Native Fixed Point DSP Agilex™ FPGA IP Core References 6. Multiply Adder Intel® FPGA IP Core References 7. ALTMULT_COMPLEX Intel® FPGA IP Core References 8. LPM_MULT Intel® FPGA IP Core References 9. LPM_DIVIDE (Divider) Intel FPGA IP Core 10. Native Floating Point DSP Agilex™ FPGA IP References 11. Native AI Optimized DSP Agilex™ FPGA IP References 12. Document Revision History for the Agilex™ 5 Variable Precision DSP Blocks User Guide
1. Agilex™ 5 Variable Precision DSP Blocks Overview x
1.1. Features 1.2. Supported Operational Modes in Agilex™ 5 Devices
1.2. Supported Operational Modes in Agilex™ 5 Devices x
1.2.1. Fixed-point Arithmetic 1.2.2. Floating-point Arithmetic
2. Agilex™ 5 Variable Precision DSP Blocks Architecture x
2.1. Fixed-point Arithmetic 2.2. Floating-point Arithmetic 2.3. Tensor Mode
2.1. Fixed-point Arithmetic x
2.1.1. Input Register Bank for Fixed-point Arithmetic 2.1.2. Pipeline Registers for Fixed-point Arithmetic 2.1.3. Pre-adder for Fixed-point Arithmetic 2.1.4. Internal Coefficient for Fixed-point Arithmetic 2.1.5. Multipliers for Fixed-point Arithmetic 2.1.6. Adder or Subtractor for Fixed-point Arithmetic 2.1.7. Accumulator, Chainout Adder, and Preload Constant for Fixed-point Arithmetic 2.1.8. Systolic Register for Fixed-point Arithmetic 2.1.9. Double Accumulation Register for Fixed-point Arithmetic 2.1.10. Output Register Bank for Fixed-point Arithmetic
2.1.7. Accumulator, Chainout Adder, and Preload Constant for Fixed-point Arithmetic x
2.1.7.1. Dynamic Chainout
2.2. Floating-point Arithmetic x
2.2.1. Input Register Bank for Floating-point Arithmetic 2.2.2. Pipeline Registers for Floating-point Arithmetic 2.2.3. Multipliers for Floating-point Arithmetic 2.2.4. Adder or Subtractor for Floating-point Arithmetic 2.2.5. Output Register Bank for Floating-point Arithmetic 2.2.6. Exception Handling for Floating-point Arithmetic
3. Agilex™ 5 Variable Precision DSP Blocks Operational Modes x
3.1. Operational Modes for Fixed-point Arithmetic 3.2. Operational Modes for Floating-point Arithmetic 3.3. Operational Modes for Tensor Mode
3.1. Operational Modes for Fixed-point Arithmetic x
3.1.1. Independent Multiplier Mode 3.1.2. Multiplier Adder Sum Mode 3.1.3. Independent Complex Multiplier 3.1.4. Systolic FIR Mode
3.1.1. Independent Multiplier Mode x
3.1.1.1. 18 × 18 or 18 × 19 Independent Multiplier 3.1.1.2. 27 × 27 Independent Multiplier
3.1.2. Multiplier Adder Sum Mode x
3.1.2.1. 8 x 8 (Unsigned) or 9 x 9 (Signed) Sum of 6 Mode
3.1.2.1. 8 x 8 (Unsigned) or 9 x 9 (Signed) Sum of 6 Mode x
3.1.2.1.1. 18 × 19 Multiplication Summed with 36-Bit Input Mode
3.1.4. Systolic FIR Mode x
3.1.4.1. Mapping Systolic Mode User View to Variable Precision Block Architecture View 3.1.4.2. 18-bit Systolic FIR Mode 3.1.4.3. 27-Bit Systolic FIR Mode
3.2. Operational Modes for Floating-point Arithmetic x
3.2.1. FP32 Single-precision Floating-point Arithmetic Functions 3.2.2. FP16 Half-precision Floating-point Arithmetic Functions 3.2.3. Multiple Floating-point Variable DSP Blocks Functions
3.2.1. FP32 Single-precision Floating-point Arithmetic Functions x
3.2.1.1. FP32 Multiplication Mode 3.2.1.2. Adder or Subtract Mode 3.2.1.3. Multiply Accumulate Mode 3.2.1.4. FP32 Vector One Mode 3.2.1.5. FP32 Vector Two Mode
3.2.2. FP16 Half-precision Floating-point Arithmetic Functions x
3.2.2.1. FP16 Supported Precision Formats 3.2.2.2. Sum of Two FP16 Multiplication Mode 3.2.2.3. Sum of Two FP16 Multiplication with FP32 Addition Mode 3.2.2.4. Sum of Two FP16 Multiplication with Accumulation Mode 3.2.2.5. FP16 Vector One Mode 3.2.2.6. FP16 Vector Two Mode 3.2.2.7. FP16 Vector Three Mode
3.2.3. Multiple Floating-point Variable DSP Blocks Functions x
3.2.3.1. Multiply-Add or Multiply-Subtract Mode 3.2.3.2. Direct Vector Dot Product 3.2.3.3. Complex Multiplication
3.3. Operational Modes for Tensor Mode x
3.3.1. Data Input Feed Preloading Method 3.3.2. Side Input Feed Preloading Method 3.3.3. Tensor Floating-point Mode 3.3.4. Tensor Fixed-point Mode 3.3.5. Tensor Accumulation Mode
3.3.3. Tensor Floating-point Mode x
3.3.3.1. Input Register Bank for Tensor Floating-point Mode 3.3.3.2. Pipeline Registers for Tensor Floating-point Mode 3.3.3.3. Cascade Signals for Tensor Floating-point Mode 3.3.3.4. Output Registers for Tensor Floating-point Mode
3.3.4. Tensor Fixed-point Mode x
3.3.4.1. Input Register Bank for Tensor Fixed-point Mode 3.3.4.2. Pipeline Registers for Tensor Fixed-point Mode 3.3.4.3. Cascade Signals for Tensor Fixed-point Mode 3.3.4.4. Output Registers for Tensor Fixed-point Mode
3.3.5. Tensor Accumulation Mode x
3.3.5.1. Input Register Bank for Tensor Accumulation Mode 3.3.5.2. Pipeline Registers for Tensor Accumulation Mode 3.3.5.3. Cascade Signals for Tensor Accumulation Mode 3.3.5.4. Output Registers for Tensor Accumulation Mode
4. Agilex™ 5 Variable Precision DSP Blocks Design Considerations x
4.1. Fixed-point Arithmetic 4.2. Floating-point Arithmetic 4.3. DSP Block Cascade Limit in Agilex™ 5 Devices
4.1. Fixed-point Arithmetic x
4.1.1. Configurations for Input, Pipeline, and Output Registers 4.1.2. Internal Coefficient and Pre-Adder for Fixed-point Arithmetic 4.1.3. Accumulator for Fixed-point Arithmetic 4.1.4. Input Cascade for Fixed-point Arithmetic 4.1.5. Chainout Adder
4.1.1. Configurations for Input, Pipeline, and Output Registers x
4.1.1.1. Restrictions for Input Registers 4.1.1.2. Restrictions for Pipeline Registers 4.1.1.3. Supported Register Configurations per Operation Modes
4.1.4. Input Cascade for Fixed-point Arithmetic x
4.1.4.1. Dynamic Scanin
4.2. Floating-point Arithmetic x
4.2.1. Configurations for Input, Pipeline, and Output Registers 4.2.2. Chainout Adder
4.2.1. Configurations for Input, Pipeline, and Output Registers x
4.2.1.1. FP32 Operation Modes Supported Register Configurations 4.2.1.2. FP16 Operation Mode Supported Register Configurations
5. Native Fixed Point DSP Agilex™ FPGA IP Core References x
5.1. Native Fixed Point DSP Agilex™ FPGA IP Release Information 5.2. Supported Operational Modes 5.3. Maximum Input Data Width for Fixed-point Arithmetic 5.4. Maximum Output Data Width for Fixed-point Arithmetic 5.5. Parameterizing Native Fixed Point DSP IP 5.6. Native Fixed Point DSP Agilex™ FPGA IP Signals 5.7. IP Migration
5.3. Maximum Input Data Width for Fixed-point Arithmetic x
5.3.1. Using Less Than 36-Bit Operand In 18 x 18 Plus 36 Mode Example
5.5. Parameterizing Native Fixed Point DSP IP x
5.5.1. Operation Mode Tab 5.5.2. Input Cascade Tab 5.5.3. Pre-adder Tab 5.5.4. Internal Coefficient Tab 5.5.5. Accumulator/Output Chaining 5.5.6. Pipelining 5.5.7. Clear Signal
5.6. Native Fixed Point DSP Agilex™ FPGA IP Signals x
5.6.1. 9 × 9 Sum of 6 Mode Signals 5.6.2. 16 × 16 Complex Multiplier Mode Signals 5.6.3. 18 × 18 Full Mode Signals 5.6.4. 18 × 18 Sum of Two Mode Signals 5.6.5. 18 × 18 Plus 36 Mode Signals 5.6.6. 18 × 18 Systolic Mode Signals 5.6.7. 27 × 27 Mode Signals
6. Multiply Adder Intel® FPGA IP Core References x
6.1. Multiply Adder Intel® FPGA IP Release Information 6.2. Features 6.3. Parameters 6.4. Signals
6.2. Features x
6.2.1. Pre-adder 6.2.2. Systolic Delay Register 6.2.3. Pre-load Constant 6.2.4. Double Accumulator
6.2.1. Pre-adder x
6.2.1.1. Pre-adder Simple Mode 6.2.1.2. Pre-adder Coefficient Mode 6.2.1.3. Pre-adder Input Mode 6.2.1.4. Pre-adder Square Mode 6.2.1.5. Pre-adder Constant Mode
6.3. Parameters x
6.3.1. General Tab 6.3.2. Extra Modes 6.3.3. Multipliers Tab 6.3.4. Preadder Tab 6.3.5. Accumulator Tab 6.3.6. Systolic/Chainout Tab 6.3.7. Pipelining Tab
7. ALTMULT_COMPLEX Intel® FPGA IP Core References x
7.1. ALTMULT_COMPLEX Intel® FPGA IP Release Information 7.2. Features 7.3. Parameters 7.4. Signals
8. LPM_MULT Intel® FPGA IP Core References x
8.1. LPM_MULT Intel® FPGA IP Release Information 8.2. Features 8.3. Parameters 8.4. Signals
8.3. Parameters x
8.3.1. General Tab 8.3.2. General 2 Tab 8.3.3. Pipelining Tab
9. LPM_DIVIDE (Divider) Intel FPGA IP Core x
9.1. LPM_DIVIDE Intel® FPGA IP Release Information 9.2. Features 9.3. Verilog HDL Prototype 9.4. VHDL Component Declaration 9.5. VHDL LIBRARY_USE Declaration 9.6. Ports 9.7. Parameters
9.7. Parameters x
9.7.1. General Tab 9.7.2. General1 Tab
10. Native Floating Point DSP Agilex™ FPGA IP References x
10.1. Native Floating Point DSP Agilex™ FPGA IP Release Information 10.2. Native Floating Point DSP Agilex™ FPGA IP Core Supported Operational Modes 10.3. Parameterizing the Native Floating Point DSP Agilex™ FPGA IP 10.4. Native Floating Point DSP Agilex™ FPGA IP Core Signals 10.5. IP Migration
10.3. Parameterizing the Native Floating Point DSP Agilex™ FPGA IP x
10.3.1. General Tab 10.3.2. Registers Tab
10.4. Native Floating Point DSP Agilex™ FPGA IP Core Signals x
10.4.1. FP32 Multiplication Mode Signals 10.4.2. FP32 Addition or Subtraction Mode Signals 10.4.3. FP32 Multiplication with Addition or Subtraction Mode Signals 10.4.4. FP32 Multiplication with Accumulation Mode Signals 10.4.5. FP32 Vector One and Vector Two Modes Signals 10.4.6. Sum of Two FP16 Multiplication Mode Signals 10.4.7. Sum of Two FP16 Multiplication with FP32 Addition Mode Signals 10.4.8. Sum of Two FP16 Multiplication with Accumulation Mode Signals 10.4.9. FP16 Vector One and Vector Two Modes Signals 10.4.10. FP16 Vector Three Mode Signals
11. Native AI Optimized DSP Agilex™ FPGA IP References x
11.1. Native AI Optimized DSP Agilex™ FPGA IP Release Information 11.2. Native AI Optimized DSP Agilex™ FPGA IP Core Supported Operational Modes 11.3. Parameterizing the Native AI Optimized DSP Agilex™ FPGA IP 11.4. Native AI Optimized DSP Agilex™ FPGA IP Core Signals 11.5. IP Migration
11.3. Parameterizing the Native AI Optimized DSP Agilex™ FPGA IP x
11.3.1. Operation Mode Tab Tensor Floating-Point Tensor Fixed-Point 11.3.2. Clock Source Enable/Clear Tab
11.4. Native AI Optimized DSP Agilex™ FPGA IP Core Signals x
11.4.1. Tensor Floating-point Mode Signals 11.4.2. Tensor Fixed-point Mode Signals 11.4.3. Tensor Accumulation Mode Signals
1. Agilex™ 5 Variable Precision DSP Blocks Overview
2. Agilex™ 5 Variable Precision DSP Blocks Architecture
3. Agilex™ 5 Variable Precision DSP Blocks Operational Modes
4. Agilex™ 5 Variable Precision DSP Blocks Design Considerations
5. Native Fixed Point DSP Agilex™ FPGA IP Core References
6. Multiply Adder Intel® FPGA IP Core References
7. ALTMULT_COMPLEX Intel® FPGA IP Core References
8. LPM_MULT Intel® FPGA IP Core References
9. LPM_DIVIDE (Divider) Intel FPGA IP Core
10. Native Floating Point DSP Agilex™ FPGA IP References
11. Native AI Optimized DSP Agilex™ FPGA IP References
12. Document Revision History for the Agilex™ 5 Variable Precision DSP Blocks User Guide

11.3.1. Operation Mode Tab

Table 152.  Operation Mode Tab
Parameter IP Generated Parameter Value Default value Description
Select Operation Mode dsp_mode

Tensor Floating-Point

Tensor Fixed-Point

Tensor Accumulation

Tensor Floating-Point

Select operation mode.

Tensor Floating-Point:

Represents and manipulates rational numbers via a minimum of 32 bits in a manner similar to scientific notation, where a number is represented with a mantissa (27bits) and an exponent (8bits). Refer to the following example.

Tensor Fixed-Point:

Represents and manipulates integers – positive and negative whole numbers. Refer to the following example.

Tensor Accumulation:

Accumulates the output results of the other DSP.
Select type of feed in dsp_side_feed_ctrl

data_feed_in

side_feed_in

data_feed_in

Control signal to select the input source feeds.

This option is available in Tensor Floating-Point and Tensor Fixed-Point.
Cascade/Accumulation/Zeroing
Enable Cascade Data In dsp_chain_tensor

Yes

No

No

Enable cascade data in port.

Used to indicate that signal that goes through the cascade data in port will be the output from another DSP that is intended to do the cascading between two or multiple DSP blocks.

Enable Cascade Data Out enable_cascade_data_out

Yes

No

No

Enable cascade data out port.

Used to indicate that the output result will flow through the cascade data out port and that it can be used for cascading two or multiple DSP blocks.

Enable 'acc_en' port enable_acc_en

Yes

No

No

A dynamic control signal to specify whether to enable the accumulation feature.

When the accumulate parameter is enabled, it will accumulate the current result to the previous accumulation result.
Enable 'zero_en' port enable_zero_en

Yes

No

No

A dynamic control signal to disable the input to the FP32 ALU or CPA adder.

Used to clear the accumulation result.

When this parameter is enabled, it will disable all the input signals from either the accumulator or the input from a cascade DSP block.

To choose either one of the input data, it must be configured with the ‘acc_en’ port value.
Single Precision Floating Point Operation
Select Single-Precision Floating-Point Operation dsp_fp32_sub_en

Addition

Subtraction

Addition

Single-Precision Floating-Point Operation on the FP32 ALU.

This option is available in Tensor Floating-Point and Tensor Accumulation.

Used to set either to perform an addition or a subtraction on accumulator.
Enable 'load_bb_one' port enable_load_bb_one

Yes

No

No

A dynamic signal to pre-load first set of register

This option is available in Tensor Floating-Point and Tensor Fixed-Point
Enable 'load_bb_two' port enable_load_bb_two

Yes

No

No

A dynamic signal to pre-load second set of register

This option is available in Tensor Floating-Point and Tensor Fixed-Point
Enable 'load_buf_sel' port enable_load_buf_sel

Yes

No

No

A dynamic control signal used to switch the set of ping pong registers to perform computation

This option is available in Tensor Floating-Point and Tensor Fixed-Point

Tensor Floating-Point

Floating point number : 1.234567, 123456.7, 0.00001234567, 1234567000000000, …
  • 1 bit for the sign
  • 8 bits for the exponent
  • 23 bits for the fraction (or mantissa)

The value of the floating point number is calculated as:

(-1)sign × (1 + fraction) × 2exponent-127

Subnormal numbers are used to represent numbers that are very close to zero, where the exponent is all zeros and has an implicit leading bit of zero instead of one.

(-1)sign × (fraction) × 2-127

Example calculation:

Given a small input value: fp32_a1 = 32'h00000001 (or in binary 00000000000000000000000000000001), which is represented as:
  • sign bit: 0
  • exponent: 00000000
  • fraction: 00000000000000000000001

As a subnormal number:

(-1)0 x (0.00000000000000000000001) x 2-126, which is considered as a relatively small number, resulting to the output to become 32'h00000000.

Tensor Fixed-Point

Fixed point number : 123.45, 1234.56, 12345.67, …

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