Embedded - Microprocessors

Part Number	Description / PDF	Quantity
AM1808BZCEA3 Texas Instruments	RISC MPU, 32-BIT, 375MHZ	153
AM5706BCBDJA Texas Instruments	J6ENTRY PG 2.0 17MM GP	0
AM3354BZCEA60 Texas Instruments	IC MPU SITARA 600MHZ 298NFBGA	0
AM1707BZKBA3 Texas Instruments	MICROPROCESSOR, 32 BIT, SITARA,	5306
AM1808BZWT4 Texas Instruments	RISC MPU, 32-BIT, 456MHZ	29506
AM1802EZCED3 Texas Instruments	AM1802 SITARA PROCESSOR: 32-BIT,	73355
AM5729BABCXA Texas Instruments	VAYU PG 2.0	240
AM3356BZCEA60 Texas Instruments	IC MPU	0
OMAP3525ECBB Texas Instruments	OMAP 3 ARCH, DSP + ARM CORTEX-A8	0
TL16PNP100AFN Texas Instruments	MICROPROCESSOR CIRCUIT PQCC44	566
AM4376BZDN100 Texas Instruments	MOD ARM CORTEX-A9 491NFBGA	0
XAM3517AZER Texas Instruments	MPU, 600MHZ, CMOS, PBGA484	0
AM3358BZCZ100 Texas Instruments	IC MPU SITARA 1.0GHZ 324NFBGA	1627
AM3358BZCZ80 Texas Instruments	IC MPU SITARA 800MHZ 324NFBGA	395
OMAP3503ECUS Texas Instruments	IC MPU OMAP-35XX 600MHZ 423FCBGA	89
OMAPL138AZCEA3 Texas Instruments	IC MPU OMAP-L1X 300MHZ 361NFBGA	0
AM3703CUSA Texas Instruments	IC MPU SITARA 800MHZ 423FCBGA	375
AM1806EZWTD4 Texas Instruments	IC MPU SITARA 456MHZ 361NFBGA	0
XAM437XAZDN100 Texas Instruments	32-BIT, 1000MHZ, CMOS, PBGA491	0
S4MF06607BSPZQQ1R Texas Instruments	32-BIT ARM CORTEX M3 MCU	100

Embedded - Microprocessors

1. Overview

Embedded microprocessors are specialized computing units designed for dedicated control, monitoring, or processing tasks within electronic systems. Unlike general-purpose CPUs, they integrate processing cores, memory interfaces, and peripheral controllers into a single chip (SoC) to optimize performance, power efficiency, and cost for specific applications. These devices form the backbone of modern IoT, automotive systems, industrial automation, and consumer electronics.

2. Main Types and Functional Classification

Type	Functional Characteristics	Application Examples
ARM Cortex-M Series	Low-power 32-bit cores with real-time capabilities, optional DSP extensions	Smart sensors, wearables, motor control
PowerPC	High reliability, floating-point units, automotive/Aerospace focused	Vehicle ECUs, avionics systems
MIPS	Efficient pipelining, 32/64-bit variants for multimedia processing	Networking equipment, set-top boxes
RISC-V	Open instruction set, modular architecture for customization	AI accelerators, edge computing devices
x86 (Embedded)	PC compatibility, high processing power with integrated GPUs	Industrial PCs, medical imaging equipment

3. Architecture and Components

A typical embedded microprocessor contains:

Processing Core(s): RISC/Complex Instruction Set architectures with 1-8 cores
Memory Subsystem: Integrated SRAM, ROM, and external DRAM controllers
Peripheral Interfaces: UART, SPI, I2C, USB, CAN, PCIe, Ethernet MAC
Real-Time Components: Watchdog timers, PWM generators, ADC/DAC modules
Power Management: Multiple sleep modes, DVFS (Dynamic Voltage/Frequency Scaling)

4. Key Technical Specifications

Parameter	Description	Importance
Clock Frequency	Operating speed (10MHz-6GHz)	Determines processing throughput
Instruction Set	RISC vs CISC architecture	Affects code density and power consumption
TDP (Thermal Design Power)	Power consumption under load (100mW-50W)	Dictates thermal management requirements
Process Node	Manufacturing technology (28nm-5nm)	Impacts performance and energy efficiency
Memory Bandwidth	Data transfer rate between core and memory	Limits performance in data-intensive tasks

5. Application Domains

Consumer Electronics: Smartphones (application processors), home appliances
Automotive: ADAS controllers, engine management systems
Industrial: PLCs (Programmable Logic Controllers), robotics
Healthcare: MRI scanners, portable diagnostic devices
Communications: 5G base stations, optical network transceivers

6. Leading Manufacturers and Products

Manufacturer	Representative Product	Key Features
ARM Holdings	Cortex-A78	128-bit NEON engine, 4nm process, 3.0GHz
Intel	Atom x6425E	Quad-core, 12W TDP, integrated Intel HD Graphics
NXP Semiconductors	i.MX 8M Plus	1.8GHz Cortex-A53, NPU for ML acceleration
Microchip	SAM9X60	32-bit ARM926EJ-S, 120MHz, ECC memory support
Qualcomm	QCS610	Hexagon DSP, Adreno GPU, AI Engine for computer vision

7. Selection Guidelines

Key considerations include:

Performance requirements vs power budget (e.g., Cortex-M55 for ultra-low-power AI)
Real-time constraints (deterministic latency for motor control applications)
Peripheral integration (CAN FD for automotive networks)
Software ecosystem (RTOS support, middleware availability)
Longevity and supply chain stability (automotive-grade qualification)

Case Study: A smart thermostat design selected NXP's MCIMX7U5 (Cortex-M4 + Cortex-A7) for its combination of real-time sensor processing and application-layer connectivity.

8. Industry Trends

Emerging directions include:

AI integration: On-chip neural processing units (NPUs) for edge ML inference
Heterogeneous computing: Multi-architecture SoCs (RISC-V + GPU + NPU)
Advanced packaging: 3D-stacked memory integration for bandwidth-intensive applications
Functional safety: ISO 26262 compliance for autonomous vehicle systems
Open ecosystems: Growth of RISC-V adoption in custom ASIC designs